Dissertations / Theses on the topic 'Modern learning theory'
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Rigtorp, Johan. "Organizational Learning Capability in a Modern Army." Thesis, Försvarshögskolan, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:fhs:diva-9159.
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Studies of organizational learning have found that military organisations can benefit from developing organizational learning. Successful implementation of organizational learning exists in the Israeli army. This study analyses the organizational learning capability of the Swedish army. By adopting the organizational learning capability theory by Visser and applying it to data collected through both documents and interviews, this study investigates the possible harmony and dissonance between the data. The findings discovered that while there is compatibility in two out of four dimensions, which is interpreted as the Swedish army having a good baseline to build their organizational learning; it also ascertained that there is a large dissonance regarding knowledge conversion. This is seen as a probable inhibitor for the implementation of organizational learning in the Swedish army. Specifically, is the lack of education in knowledge conversion seen as a large threat to the organization successfully implementing organizational learning. The study contributes to the research field with a comparison of the normative level and reality; in this it contributes with an understanding of which parts can be considered to facilitate and inhibit organizational learning. Furthermore, it gives the Swedish Army several recommendations to accelerate their capabilities in organizational learning.
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Lord, Mary E. "How a Learning Orientation, Modern Portfolio Theory and Absorptive Capacity Contribute to University Endowment Performance." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1333676043.
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Smith, Zena Diane. "Modern witchcraft in suburban Australia: how and what witches learn." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/383.
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Existing anthropological research and discussion related to contemporary Wiccan and Witchcraft practice is growing and indeed has been explored by anthropologists and other writers from the northern and southern hemispheres. However, there has been limited discourse on how and what Western Australian Wiccans and Witches learn. This ethnographic research fills that gap by exploring, in two separate sections, how Wiccans and Witches have developed relevant skills in a social learning structure and what ritual practice they have learnt as a result. The thesis proposes that the current theories of learning and ritual fail to adequately describe the social processes and outcomes observed.In the first section, focusing on how the participants learn, I argue that cognitive, behavioural and humanist learning theories as well as the most relevant social learning theory, Communities of Practice, fail to explain adequately the holistic learning processes with which the Wiccans and Witches are engaged. Instead I propose a new and complementary theory of learning that I identify as 'Whole Person’ theory that more effectively describes the holistic and intuitive nature of learning the research participants undertook.In the second section I go further to show that the existing theories of ritual fail to explore and consider ritual as a product or outcome of learning and instead focus heavily on ritual either as a process contributing to and reflecting the social order in which it takes place or they describe the structure of ritual. This research shows that ritual can be both a process of a social group as well as a product and an end result of learning and social interaction. The ethnographic materials presented extend our understanding of both learning and ritual.
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Barnes, Ann. "Student modern foreign languages teachers learning to teach : beliefs, attitudes and the development of a methodological landscape." Thesis, University of Warwick, 2003. http://wrap.warwick.ac.uk/1229/.
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This study examines the motivations, beliefs and attitudes of beginning modern foreign languages teachers towards foreign language teaching and learning during their initial teacher education and the changes in attitudes towards and beliefs about their subject and its methodology. In so doing, the study uncovers the students' initial and developing methodological landscapes. The scope of the study is unusual in its breadth of response'a! nd in its multi-method approach incorporating qualitative and quantitative data collection and analysis, identifying interconnections in the data. A total of 235 student teachers' responses contribute to the research: the pre-course beliefs of eight cohorts of beginning teachers are analysed to establish a basis for exploring any change. The research subsequently adopts a longitudinal approach, where data' is obtained through a series of ten snapshot questionnaires administered to three separate cohorts of student teachers. This data is supplemented by smaller samples from two cohorts in a different initial teacher education institution. It is further triangulated through twelve group discussions on video from two cohorts. Analysis is of whole and aggregated cohorts and also by gender and native speaker. Views indicated by the beginning teachers' stated perceptions of their development incorporate elements from a variety of learning-to-teach theories. Some more generic themes which emerge as important in student teachers' thinking throughout the year include the desire for fantasy solutions and the process of future-wishing, both of which serve as attempts to avoid a true (and difficult) developmental process. Stability of fundamental beliefs is evident, but substantial change occurs in perceptions of items contributing to the methodological landscape, particularly in the areas of target language and grammar.
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Hoogland, Anna. "När övningen blir meningsfull : En självobservationsstudie av instuderingsprocessen av en modern operaaria." Thesis, Karlstads universitet, Institutionen för konstnärliga studier (from 2013), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-67095.
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Syftet med föreliggande studie är att studera min lärandeprocess vid instuderingen av en modern operaaria. Studien utgår från ett designteoretiskt perspektiv på lärande med begreppen design, multimodalitet och semiotiska resurser i fokus. Processen pågick under tre veckor under hösten 2017 med totalt 14 övningspass. För att dokumentera lärandeprocessen under dessa övningspass användes metoderna videoobservation och loggbok. I resultatet beskrivs lärandeprocessens design med hjälp av två teman: Kreativt ostrukturerad och Strävan efter helhetsupplevelsen. Resultatet visar också att kroppsliga resurser användes under lärandeprocessens gång för att förkroppsliga musikaliska parametrar, medan materiella resurser användes för att på ett konkret sätt komplettera de kroppsliga resurserna. I diskussionen diskuteras resultatet i förhållande till ett designteoretiskt perspektiv på lärande samt till tidigare forskning om övning och instudering. Några aspekter som berörs är betydelsen av en ljudande förlaga att lyssna till kopplat till helhetsaspekten samt den kreativt ostrukturerade lärandeprocessens inverkan på progressionen i övningen.The purpose of the present study is to examine my own learning process with a modern opera aria. The study is based on a design theoretical perspective on learning with focus on the concepts design, multimodality and semiotic resources. The process went on for three weeks during the autumn 2017 with a total of 14 practising sessions. To be able to document the learning process during these practising sessions, the methods video observation and logbook were used. The design of the learning process is described in the result by two different themes; Creatively unstructured and The endeavour to the overall experience. The result also shows that physical resources were used during the learning process to embody musical parameters, while material resources became a complement to the physical resources in a concrete way. In the last section, the result is discussed relative to a design theoretical perspective on learning and to previous research on practise and rehearsal methods. Some aspects are the meaning of a sounding example to listen to relative to the aspect of the overall experience, and the creatively unstructured learning process and its impact on the practice progression.
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Yang, Wan Chi (Ada Yang). "The enlightened Chinese characters : a cognitive approach of computer assisted Chinese character learning." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/2428.
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Thesis (MPhil (Modern Foreign Languages))--University of Stellenbosch, 2006.With continuing advances in technology, computer-assisted instruction provides opportunities for individualized, interactive learning. In the research paper, I employed the theoretical framework of CALL and the philosophy of cognitive psychhology to promote learner autonomy in the second language aquisition of Chinese...
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Duwe, Astrid, and Durán Lisa Johansson. "Entreprenöriellt lärande i moderna språk : - möjligheter och hinder." Thesis, Högskolan i Halmstad, Sektionen för lärarutbildning (LUT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-15722.
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Den nya läroplanen för grundskolan 2011 uppmanar alla lärare i den svenska skolan att stärka ett entreprenöriellt förhållningssätt hos eleverna. Enligt regeringens Strategi för entreprenörskap inom utbildningsområdet ska entreprenörskap löpa som en röd tråd genom hela skolan. Medan en del forskning är gjord på gymnasienivå och utifrån ett elevperspektiv så finns det dock i dagsläget relativt få undersökningar angående grundskolan och lärarnas perspektiv. Förutsättningarna för införandet av entreprenöriellt lärande är oklara. En viktig förutsättning är dock att lärarna är bekanta med begrepp som entreprenörskap och entreprenöriellt lärande. Studien syftar till att undersöka hur lärare i moderna språk uppfattar begreppet entreprenöriellt lärande, och vilka möjligheter och hinder de ser med att tillämpa entreprenöriella arbetsformer i språkundervisningen. Genom kvalitativa intervjuer undersöks hur sju lärare i moderna språk med olika uppfattningar resonerar kring entreprenöriellt lärande i sina ämnen. Lärarna finner det viktigt att utveckla egenskaper hos elever såsom att våga, förmågan att ta initiativ och omsätta idéer till handlingar men de ser sig också ställda inför en del svårigheter med att tillämpa de nya metoderna. Ett intressant utfall var att lärarnas uppfattning av sina förutsättningar hänger ihop väldigt starkt med deras uppfattning av och förhållningssätt till EL. Studien visar att det krävs större satsningar och framförallt mer forskning och beprövad erfarenhet inom fältet.The new curriculum for primary education 2011 calls on all teachers in Swedish schools to strengthen an entrepreneurial attitude in students. According to the government's Strategy for entrepreneurship in education, entrepreneurship should permeate the whole school system. While some research has been done at secondary level and from a student perspective, relatively few studies have investigated the issue from a teachers' perspective and at primary level. The prerequisites for the introduction of entrepreneurial learning are somewhat unclear. An important condition is, however, that teachers are familiar with concepts such as entrepreneurship and entrepreneurial learning. This study aims to examine how teachers of modern languages perceive the concept of entrepreneurial learning, and what opportunities and obstacles they see to the application of entrepreneurial work in language teaching. By using qualitative interviews, the study investigates how seven teachers of modern languages with different experiences of the methods reason about enterprise learning in their subjects. The teachers find it important to help their students develop such characteristics as daring, the ability to take initiatives and turn their ideas into actions, but they also find themselves facing some difficulties in applying the new methods. An interesting outcome was that the teachers’ perceptions of their conditions are closely connected to their perceptions of and attitudes towards enterprise learning. The study shows that greater efforts and most importantly, more research and documented experience is needed in the field.
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Sloan, Robert Hal. "Computational learning theory : new models and algorithms." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/38339.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Includes bibliographical references (leaves 116-120).
by Robert Hal Sloan.
Ph.D.
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Leo, Valentine. "Incorporating learning theory into existing systems engineering models." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37661.
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Approved for public release; distribution is unlimitedSystems engineering and learning theories are two major disciplines that involve preparing people to solve problems. While learning theories and their elements are apparent in the field of systems engineering, limited work has been performed on the interactions and relationship between these two disciplines. This thesis aims to establish and discuss a relationship between systems engineering and learning theories over the key phases of a systems life cycle. This thesis discusses how organizations can use the information attained from the collaborative approach between systems engineering and learning theories to leverage practitioners work quality, capability, and decisions to help justify and improve key systems parameters.
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Gaus, Eric. "Macroeconomic models with endogenous learning." Thesis, University of Oregon, 2010. http://hdl.handle.net/1794/10868.
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xi, 87 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.The behavior of the macroeconomy and monetary policy is heavily influenced by expectations. Recent research has explored how minor changes in expectation formation can change the stability properties of a model. One common way to alter expectation formation involves agents' use of econometrics to form forecasting equations. Agents update their forecasts based on new information that arises as the economy progresses through time. In this way agents "learn" about the economy. Previous learning literature mostly focuses on agents using a fixed data size or increasing the amount of data they use. My research explores how agents might endogenously change the amount of data they use to update their forecast equations. My first chapter explores how an established endogenous learning algorithm, proposed by Marcet and Nicolini, may influence monetary policy decisions. Under rational expectations (RE) determinacy serves as the main criterion for favoring a model or monetary policy rule. A determinant model need not result in stability under an alternative expectation formation process called learning. Researchers appeal to stability under learning as a criterion for monetary policy rule selection. This chapter provides a cautionary tale for policy makers and reinforces the importance of the role of expectations. Simulations appear stable for a prolonged interval of time but may suddenly deviate from the RE solution. This exotic behavior exhibits significantly higher volatility relative to RE yet over long simulations remains true to the RE equilibrium. In the second chapter I address the effectiveness of endogenous gain learning algorithms in the presence of occasional structural breaks. Marcet and Nicolini's algorithm relies on agents reacting to forecast errors. I propose an alternative, which relies on agents using statistical information. The third chapter uses standard macroeconomic data to find out whether a model that has non-rational expectations can outperform RE. I answer this question affirmatively and explore what learning means to the economy. In addition, I conduct a Monte Carlo exercise to investigate whether a simple learning model does, empirically, imbed an RE model. While theoretically a very small constant gain implies RE, empirically learning creates bias in coefficient estimates.
Committee in charge: George Evans, Co-Chairperson, Economics; Jeremy Piger, Co-Chairperson, Economics; Shankha Chakraborty, Member, Economics; Sergio Koreisha, Outside Member, Decision Sciences
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Calderone, Carli E. "Stem Cell Research: Science Education and Outreach." Miami University Honors Theses / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=muhonors1268751337.
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Zantedeschi, Valentina. "A Unified View of Local Learning : Theory and Algorithms for Enhancing Linear Models." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES055/document.
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Dans le domaine de l'apprentissage machine, les caractéristiques des données varient généralement dans l'espace des entrées : la distribution globale pourrait être multimodale et contenir des non-linéarités. Afin d'obtenir de bonnes performances, l'algorithme d'apprentissage devrait alors être capable de capturer et de s'adapter à ces changements. Même si les modèles linéaires ne parviennent pas à décrire des distributions complexes, ils sont réputés pour leur passage à l'échelle, en entraînement et en test, aux grands ensembles de données en termes de nombre d'exemples et de nombre de fonctionnalités. Plusieurs méthodes ont été proposées pour tirer parti du passage à l'échelle et de la simplicité des hypothèses linéaires afin de construire des modèles aux grandes capacités discriminatoires. Ces méthodes améliorent les modèles linéaires, dans le sens où elles renforcent leur expressivité grâce à différentes techniques. Cette thèse porte sur l'amélioration des approches d'apprentissage locales, une famille de techniques qui infère des modèles en capturant les caractéristiques locales de l'espace dans lequel les observations sont intégrées.L'hypothèse fondatrice de ces techniques est que le modèle appris doit se comporter de manière cohérente sur des exemples qui sont proches, ce qui implique que ses résultats doivent aussi changer de façon continue dans l'espace des entrées. La localité peut être définie sur la base de critères spatiaux (par exemple, la proximité en fonction d'une métrique choisie) ou d'autres relations fournies, telles que l'association à la même catégorie d'exemples ou un attribut commun. On sait que les approches locales d'apprentissage sont efficaces pour capturer des distributions complexes de données, évitant de recourir à la sélection d'un modèle spécifique pour la tâche. Cependant, les techniques de pointe souffrent de trois inconvénients majeurs :ils mémorisent facilement l'ensemble d'entraînement, ce qui se traduit par des performances médiocres sur de nouvelles données ; leurs prédictions manquent de continuité dans des endroits particuliers de l'espace ; elles évoluent mal avec la taille des ensembles des données. Les contributions de cette thèse examinent les problèmes susmentionnés dans deux directions : nous proposons d'introduire des informations secondaires dans la formulation du problème pour renforcer la continuité de la prédiction et atténuer le phénomène de la mémorisation ; nous fournissons une nouvelle représentation de l'ensemble de données qui tient compte de ses spécificités locales et améliore son évolutivité. Des études approfondies sont menées pour mettre en évidence l'efficacité de ces contributions pour confirmer le bien-fondé de leurs intuitions. Nous étudions empiriquement les performances des méthodes proposées tant sur des jeux de données synthétiques que sur des tâches réelles, en termes de précision et de temps d'exécution, et les comparons aux résultats de l'état de l'art. Nous analysons également nos approches d'un point de vue théorique, en étudiant leurs complexités de calcul et de mémoire et en dérivant des bornes de généralisation serréesIn Machine Learning field, data characteristics usually vary over the space: the overall distribution might be multi-modal and contain non-linearities.In order to achieve good performance, the learning algorithm should then be able to capture and adapt to these changes. Even though linear models fail to describe complex distributions, they are renowned for their scalability, at training and at testing, to datasets big in terms of number of examples and of number of features. Several methods have been proposed to take advantage of the scalability and the simplicity of linear hypotheses to build models with great discriminatory capabilities. These methods empower linear models, in the sense that they enhance their expressive power through different techniques. This dissertation focuses on enhancing local learning approaches, a family of techniques that infers models by capturing the local characteristics of the space in which the observations are embedded. The founding assumption of these techniques is that the learned model should behave consistently on examples that are close, implying that its results should also change smoothly over the space. The locality can be defined on spatial criteria (e.g. closeness according to a selected metric) or other provided relations, such as the association to the same category of examples or a shared attribute. Local learning approaches are known to be effective in capturing complex distributions of the data, avoiding to resort to selecting a model specific for the task. However, state of the art techniques suffer from three major drawbacks: they easily memorize the training set, resulting in poor performance on unseen data; their predictions lack of smoothness in particular locations of the space;they scale poorly with the size of the datasets. The contributions of this dissertation investigate the aforementioned pitfalls in two directions: we propose to introduce side information in the problem formulation to enforce smoothness in prediction and attenuate the memorization phenomenon; we provide a new representation for the dataset which takes into account its local specificities and improves scalability. Thorough studies are conducted to highlight the effectiveness of the said contributions which confirmed the soundness of their intuitions. We empirically study the performance of the proposed methods both on toy and real tasks, in terms of accuracy and execution time, and compare it to state of the art results. We also analyze our approaches from a theoretical standpoint, by studying their computational and memory complexities and by deriving tight generalization bounds
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Page-Shipp, R., and Niekerk C. Van. "Mental models in the learning and teaching of music theory concepts." Journal for New Generation Sciences, Vol 11, Issue 2: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/637.
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Published ArticleA retired physicist attempting to master elements of music theory in a short time found the Mental Model of the keyboard layout invaluable in overcoming some of the related learning challenges and this has been followed up in collaboration with a professor of Music Education. Possible cognitive mechanisms for his response are discussed and it is concluded that his engrained learning habits, which emphasise models as found in physics, are potentially of wider applicability. A survey of the use of Mental Models among competent young musicians indicated that although various models are widely used, this is largely subconscious. The practical question of whether exposure of students to the keyboard would assist them in mastering music theory remains unresolved.
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Chen, Tao. "Search-based learning of latent tree models /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHEN.
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Ghachem, Montasser. "Essays in Evolutionary Game Theory." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-132433.
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Evolutionary game theory tries to explain the emergence of stable behaviors observed in human and animal societies. Prominent examples of such behaviors are cooperative and conformist behaviors. In the first part of the thesis, we develop a model of indirect reciprocity with institutional screening to study how institutions may promote cooperative behavior. We show that cooperation can emerge if screening institutions are sufficiently reliable at identifying cooperators. The second part presents a large-population learning model in which individuals update their beliefs through time. In the model, only one individual updates his beliefs each period. We show that a population, playing a game with two strategies, eventually learns to play a Nash equilibrium. We focus on coordination games and prove that a unique behavior arises both when players use myopic and perturbed best replies. The third part studies the payoff calculation in an evolutionary setting. By introducing mutual consent as a requirement for game play, we provide a more realistic alternative way to compute payoffs.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.
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Słowiński, Witold. "Autonomous learning of domain models from probability distribution clusters." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211059.
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Nontrivial domains can be difficult to understand and the task of encoding a model of such a domain can be difficult for a human expert, which is one of the fundamental problems of knowledge acquisition. Model learning provides a way to address this problem by allowing a predictive model of the domain's dynamics to be learnt algorithmically, without human supervision. Such models can provide insight about the domain to a human or aid in automated planning or reinforcement learning. This dissertation addresses the problem of how to learn a model of a continuous, dynamic domain, from sensory observations, through the discretisation of its continuous state space. The learning process is unsupervised in that there are no predefined goals, and it assumes no prior knowledge of the environment. Its outcome is a model consisting of a set of predictive cause-and-effect rules which describe changes in related variables over brief periods of time. We present a novel method for learning such a model, which is centred around the idea of discretising the state space by identifying clusters of uniform density in the probability density function of variables, which correspond to meaningful features of the state space. We show that using this method it is possible to learn models exhibiting predictive power. Secondly, we show that applying this discretisation process to two-dimensional vector variables in addition to scalar variables yields a better model than only applying it to scalar variables and we describe novel algorithms and data structures for discretising one- and two-dimensional spaces from observations. Finally, we demonstrate that this method can be useful for planning or decision making in some domains where the state space exhibits stable regions of high probability and transitional regions of lesser probability. We provide evidence for these claims by evaluating the model learning algorithm in two dynamic, continuous domains involving simulated physics: the OpenArena computer game and a two-dimensional simulation of a bouncing ball falling onto uneven terrain.
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Yang, Ying. "Discretization for Naive-Bayes learning." Monash University, School of Computer Science and Software Engineering, 2003. http://arrow.monash.edu.au/hdl/1959.1/9393.
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Pardos, Zachary Alexander. "Predictive Models of Student Learning." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/185.
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In this dissertation, several approaches I have taken to build upon the student learning model are described. There are two focuses of this dissertation. The first focus is on improving the accuracy with which future student knowledge and performance can be predicted by individualizing the model to each student. The second focus is to predict how different educational content and tutorial strategies will influence student learning. The two focuses are complimentary but are approached from slightly different directions. I have found that Bayesian Networks, based on belief propagation, are strong at achieving the goals of both focuses. In prediction, they excel at capturing the temporal nature of data produced where student knowledge is changing over time. This concept of state change over time is very difficult to capture with classical machine learning approaches. Interpretability is also hard to come by with classical machine learning approaches; however, it is one of the strengths of Bayesian models and aids in studying the direct influence of various factors on learning. The domain in which these models are being studied is the domain of computer tutoring systems, software which uses artificial intelligence to enhance computer based tutorial instruction. These systems are growing in relevance. At their best they have been shown to achieve the same educational gain as one on one human interaction. Computer tutors have also received the attention of White House, which mentioned an tutoring platform called ASSISTments in its National Educational Technology Plan. With the fast paced adoption of these data driven systems it is important to learn how to improve the educational effectiveness of these systems by making sense of the data that is being generated from them. The studies in this proposal use data from these educational systems which primarily teach topics of Geometry and Algebra but can be applied to any domain with clearly defined sub-skills and dichotomous student response data. One of the intended impacts of this work is for these knowledge modeling contributions to facilitate the move towards computer adaptive learning in much the same way that Item Response Theory models facilitated the move towards computer adaptive testing.
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Shon, Aaron P. "Bayesian cognitive models for imitation /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7013.
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Sgroi, Daniel. "Theories of learning in economics." Thesis, University of Oxford, 2000. http://ora.ox.ac.uk/objects/uuid:b8d832af-57e7-45c2-a846-b69de3d25ec0.
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How should we model learning behaviour in economic agents? This thesis addresses this question in two distinct ways. In the first set of chapters the assumption is that agents learn through the observation of others. They use Bayesian updating which together with specific informational assumptions can generate the problem known as herding with the potential for significant welfare losses. In the final set of chapters the agent is instead modelled as learning by example. Here the agent cannot learn by observing others, but has a pool of experience to fall back on. This allows us to examine how an economic agent will perform if he sees a particular economic situation (or game) for the first time, but has experience of playing related games. The tool used to capture the notion of learning through example is a neural network. Throughout the thesis the central theme is that economic agents will naturally use as much information as they can to help them make decisions. In many cases this should mean they take into consideration others' actions or their own experiences in similar but non-identical situations. Learning throughout the thesis will be rational or bounded-rational in the sense that either the best possible way to learn will be utilized (so players achieve full rational play, for example, through Bayesian updating), or a suitable local error-minimizing algorithm will be developed (for example, a rule of thumb which optimizes play in a subclass of games, but not in the overall set of possible games). Several themes permeate the whole thesis, including the scope for firms or planners to manipulate the information that is used by agents for their own ends, the role of rules of thumb, and the realism of current theories of learning in economics.
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Van, der Merwe Rudolph. "Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models." Full text open access at:, 2004. http://content.ohsu.edu/u?/etd,8.
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MacLellan, Christopher J. "Computational Models of Human Learning: Applications for Tutor Development, Behavior Prediction, and Theory Testing." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1054.
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Intelligent tutoring systems are effective for improving students’ learning outcomes (Bowen et al., 2013; Koedinger & Anderson, 1997; Pane et al., 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray, 1999, 2003). In this thesis, I explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, to support tutor development. In particular, I investigate their use for authoring expert-models via demonstrations and feedback (Matsuda et al., 2014), predicting student behavior within tutors (VanLehn et al., 1994), and for testing alternative learning theories (MacLellan, Harpstead, Patel, & Koedinger, 2016). To support these investigations, I present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning and enables the generation and testing of alternative models. I use this architecture to create two models: the DECISION TREE model, which non- incrementally learns when to apply its skills, and the TRESTLE model, which instead learns incrementally. Both models both draw on the same small set of prior knowledge for all simulations (six operators and three types of relational knowledge). Despite their limited prior knowledge, I demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of achieving human-level performance in seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). I show that the models are capable of predicting which versions of a fraction arithmetic and box and arrows tutors are more effective for human students’ learning. Further, I use a mixedeffects regression analysis to evaluate the fit of the models to the available human data and show that across all seven domains the TRESTLE model better fits the human data than the DECISION TREE model, supporting the theory that humans learn the conditions under which skills apply incrementally, rather than non-incrementally as prior work has suggested (Li, 2013; Matsuda et al., 2009). This work lays the foundation for the development of a Model Human Learner— similar to Card, Moran, and Newell’s (1986) Model Human Processor—that encapsulates psychological and learning science findings in a format that researchers and instructional designers can use to create effective tutoring systems.
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Álvarez, Robles Enrique Josué. "Supervised Learning models with ice hockey data." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167718.
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The technology developments of the last years allow measuring data in almost every field and area nowadays, especially increasing the potential for analytics in branches in which not much analytics have been done due to complicated data access before. The increased number of interest in sports analytics is highly connected to the better technology now available for visual and physical sensors on the one hand and sports as upcoming economic topic holding potentially large revenues and therefore investing interest on the other hand. With the underlying database, precise strategies and individual performance improvements within the field of professional sports are no longer a question of (coach)experience but can be derived from models with statistical accuracy. This thesis aims to evaluate if the available data together with complex and simple supervised machine learning models could generalize from the training data to unseen situations by evaluating performance metrics. Data from games of the ice hockey team of Linköping for the season 2017/2018 is processed with supervised learning algorithms such as binary logistic regression and neural networks. The result of this first step is to determine the strategies of passes by considering both, attempted but failed and successful shots on goals during the game. For that, the original, raw data set was aggregated to game-specific data. After having detected the distinct strategies, they are classified due to their rate of success.
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Grimes, David B. "Learning by imitation and exploration : Bayesian models and applications in humanoid robotics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6879.
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Vyroubalová, Ivana. "Moderní metody výuky Teorie obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412785.
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This diploma thesis is about design and implementation of an e-learning system to help the students in daily course of Circuits Theory. This system works with different requirements based on special rights for appropriate user-groups. Implementation of the system requires support the specific mathematical formulas, creation of study materials and functions for testing knowledges in Circuits Theory and in Safety work on electrical devices. The system is implemented using XHTML and PHP, the design using cascade style sheets CSS. The educational material is created in XML, MathML and XSL languages and tests are saved in a database. The database is designed using the UML language and implemented in MySQL database system. This system is a part of diploma thesis.
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Wenzelburger, Jan. "Learning in economic systems with expectations feedback." Berlin Heidelberg New York Springer, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2668403&prov=M&dok_var=1&dok_ext=htm.
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Lloyd, James Robert. "Representation, learning, description and criticism of probabilistic models with applications to networks, functions and relational data." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709264.
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Batista, Silvia Cristina Freitas. "M-learnmat : modelo pedagógico para atividades de m-learning em matemática." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/48916.
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M-learning (mobile learning) é o campo de pesquisa que investiga como os dispositivos móveis podem contribuir para a aprendizagem. Na presente tese é proposto o M-learnMat, um modelo pedagógico para atividades de m-learning em Matemática. O mesmo tem por objetivo orientar práticas educativas que envolvam o uso (não exclusivo) de dispositivos móveis no Ensino Superior e é fundamentado na Teoria da Atividade. Nessa teoria, o foco está nas atividades que os indivíduos desenvolvem e nas relações diversas que decorrem destas. Assim, o modelo tem seu diferencial no fato de relacionar m-learning, Matemática do Ensino Superior e Teoria da Atividade, visando contribuir para a organização, desenvolvimento e análise de atividades pedagógicas. Para a elaboração do M-learnMat, além da revisão bibliográfica, foi realizada uma pesquisa exploratória e um estudo de caso piloto. Essas ações forneceram dados que permitiram compreender melhor algumas questões relacionadas ao uso educacional de celulares. Para a experimentação do modelo foram organizados dois estudos de caso com alunos do Ensino Superior. Os mesmos ocorreram durante o primeiro semestre letivo de 2011, na disciplina de Cálculo I, com utilização dos celulares dos próprios alunos. Para a coleta e análise dos dados foi promovida uma pesquisa mista, envolvendo abordagens qualitativas e quantitativas, em função das características dos dados. A experimentação sinalizou que o M-learnMat tem potencial para orientar as atividades a que se destina, colaborando para que as mesmas sejam desenvolvidas segundo estratégias definidas.M-learning (mobile learning) is a field of research that investigates how mobile devices can contribute to learning. This thesis presents M-learnMat, a pedagogical model for m-learning Math activities. This model, developed with support of the Activity Theory, aims at orienting educational practices that involve the use (non-exclusive) of mobile devices in graduation courses. The Activity Theory focuses on the activities developed by individuals and in the various relationships resulting from them. Thus, the model is distinctive as it comprises m-learning, Mathematics and the Activity Theory with the purpose of contributing to the organization, development and analysis of pedagogical activities. Besides the literature review, the development of M-learnMat included exploratory research and a pilot case study. These actions provided data that allowed for a better understanding of some issues related to the educational use of cell phones. For model experimentation, two case studies were carried with college level students. These took place in the first semester of 2011, in Calculus I, in which students used their cell phones. For data collection, a mixed methods research (quantitative and qualitative) was used due to the characteristics of the data. The experimentation indicated that M-learnMat has the potential to guide activities, and collaborate for their application according to defined strategies.
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Al-Shehri, Abdullah Saeed. "Drawing on possible self theory to explore the influence of subjectivity on individual learning and employees' attitudes toward learning behaviours popularized by two learning organization models." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42870.
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Drawing on possible self theory, this is a qualitative study that seeks to explore two major connected assumptions. The first is whether diverse possible selves can generate a wide variety of individual learning experiences. The second which the present study seeks to explore is the joint influence of the latter two (i.e. possible selves and the individual learning experiences generated therefrom) on employees' attitudes towards learning behaviours popularized by two LO models: Senge's model and Marsick and Watkins' model. In setting the theoretical scene, the researcher argues that such models have only mildly considered the complex issues of self and subjectivity, and suggests that failure to realize the ideal of the learning organization may be partially explained by failure to acknowledge the powerful role of subjectivity in generating different individual learning experiences. In this context, possible self theory has been employed as a means to understand individuals' subjectivities and how they might influence attitudes towards formal learning behaviours associated with two LO models. This is the main contribution the present study seeks to achieve. The sample of the study consisted of 19 employees working for a well-known Saudi public corporation. A semi-structured interview was used to elicit participants' responses after which those were explored and discussed. The findings of the study generally support the need to acknowledge the centrality of subjectivity in generating diverse learning experiences across the same organization. They also reveal the idiosyncratic nature of individual learning in a ways that challenge formal organizational learning policies and popular notions on the homogeneity of organizational cultures. The implications derived thereof for organizations, individual learning, and the LO concept are detailed in the concluding chapter.
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Bhat, Sooraj. "Syntactic foundations for machine learning." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47700.
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Machine learning has risen in importance across science, engineering, and business in
recent years. Domain experts have begun to understand how their data analysis problems
can be solved in a principled and efficient manner using methods from machine learning,
with its simultaneous focus on statistical and computational concerns. Moreover, the data
in many of these application domains has exploded in availability and scale, further underscoring the need for algorithms which find patterns and trends quickly and correctly.
However, most people actually analyzing data today operate far from the expert level.
Available statistical libraries and even textbooks contain only a finite sample of the possibilities afforded by the underlying mathematical principles. Ideally, practitioners should
be able to do what machine learning experts can do--employ the fundamental principles to
experiment with the practically infinite number of possible customized statistical models as
well as alternative algorithms for solving them, including advanced techniques for handling
massive datasets. This would lead to more accurate models, the ability in some cases to
analyze data that was previously intractable, and, if the experimentation can be greatly
accelerated, huge gains in human productivity.
Fixing this state of affairs involves mechanizing and automating these statistical and
algorithmic principles. This task has received little attention because we lack a suitable
syntactic representation that is capable of specifying machine learning problems and solutions, so there is no way to encode the principles in question, which are themselves a
mapping between problem and solution. This work focuses on providing the foundational
layer for enabling this vision, with the thesis that such a representation is possible. We
demonstrate the thesis by defining a syntactic representation of machine learning that is
expressive, promotes correctness, and enables the mechanization of a wide variety of useful
solution principles.
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Wang, Siwen. "Orbital Level Understanding of Adsorbate-Surface Interactions in Metal Nanocatalysis." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98923.
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We develop a theoretical framework for a priori estimation of catalytic activity of metal nanoparticles using geometry-based reactivity descriptors of surface atoms and kinetic analysis of reaction pathways at various types of active sites. We show that orbitalwise coordination numbers 𝐶𝑁α (α = 𝑠 or 𝑑) can be used to predict chemical reactivity of a metal site (e.g., adsorption energies of critical reaction intermediates) by being aware of the neighboring chemical environment, outperforming their regular (𝐶𝑁) and generalized (𝐶̅𝑁̅) counterparts with little added computational cost. Here we include two examples to illustrate this method: CO oxidation on Au (5𝑑¹⁰6𝑠¹) and O₂ reduction on Pt (5𝑑⁹6𝑠¹). We also employ Bayesian learning and the Newns-Anderson model to advance the fundamental understanding of adsorbate-surface interactions on metal nanocatalysts, paving the path toward adsorbate-specific tuning of catalysis.Doctor of Philosophy
The interactions between reaction intermediates and catalysts should be neither too strong nor too weak for catalytic optimization. This Sabatiers principle arising from the scaling relations among the energetics of reacting species at geometrically similar sites, provides the conceptual basis for designing improved catalysts, but imposes volcano-type limitations on the attainable catalytic activity and selectivity. One of the greatest challenges faced by the catalysis community today is how to develop design strategies and ultimately predictive models of catalytic systems that could circumvent energy scaling relations. This work brings the quantum-chemical modeling and machine learning technique together and develops a novel stochastic modeling approach to rationally design the catalysts with desired properties and bridges our knowledge gap between the empirical kinetics and atomistic mechanisms of catalytic reactions.
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Ilisei, Iustina-Narcisa. "A machine learning approach to the identification of translational language : an inquiry into translationese learning models." Thesis, University of Wolverhampton, 2012. http://hdl.handle.net/2436/299371.
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In the world of Descriptive Translation Studies, translationese refers to the specific traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is different from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are no universally recognised characteristics of translational language, nor universally recognised patterns likely to occur within translational language. This thesis addresses these issues, with a less used approach in the eld of Descriptive Translation Studies, by investigating the nature of translational language from a machine learning perspective. While the main focus is on analysing translationese, this thesis investigates two related sub-hypotheses: simplication and explicitation. To this end, a multilingual learning framework is designed and implemented for the identification of translational language. The framework is modelled as a categorisation task, the learning techniques having the major goal to automatically learn to distinguish between translated and non-translated texts. The second and third major goals of this research are the retrieval of the recurring patterns that are revealed in the process of solving the task of categorisation, as well as the ranking of the most in uential characteristics used to accomplish the learning task. These aims are ful lled by implementing a system that adopts the machine learning methodology proposed in this research. The learning framework proves to be an adaptable multilingual framework for the investigation of the nature of translational language, its adaptability being illustrated in this thesis by applying it to the investigation of two languages: Spanish and Romanian. In this thesis, di erent research scenarios and learning models are experimented with in order to assess to what extent translated texts can be diff erentiated from non-translated texts in certain contexts. The findings show that machine learning algorithms, aggregating a large set of potentially discriminative characteristics for translational language, are able to diff erentiate translated texts from non-translated ones with high scores. The evaluation experiments report performance values such as accuracy, precision, recall, and F-measure on two datasets. The present research is situated at the con uence of three areas, more precisely: Descriptive Translation Studies, Machine Learning and Natural Language Processing, justifying the need to combine these elds for the investigation of translationese and translational hypotheses.
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Kidd, Kirsty. "A comparative analysis of Purkinje cells across species combining modelling, machine learning and information theory." Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/21078.
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There have been a number of computational modelling studies that aim to replicate the cerebellar Purkinje cell, though these typically use the morphology of rodent cells. While many species, including rodents, display intricate dendritic branching, it is not a universal feature among Purkinje cells. This study uses morphological reconstructions of 24 Purkinje cells from seven species to explore the changes that occur to the cell through evolution and examine whether this has an effect on the processing capacity of the cell. This is achieved by combining several modes of study in order to gain a comprehensive overview of the variations between the cells in both morphology and behaviour. Passive and active computational models of the cells were created, using the same electrophysiological parameters and ion channels for all models, to characterise the voltage attenuation and electrophysiological behaviour of the cells. These results and several measures of branching and size were then used to look for clusters in the data set using machine learning techniques. They were also used to visualise the differences within each species group. Information theory methods were also employed to compare the estimated information transfer from input to output across each cell. Along with a literature review into what is known about Purkinje cells and the cerebellum across the phylogenetic tree, these results show that while there are some obvious differences in morphology, the variation within species groups in electrophysiological behaviour is often as high as between them. This suggests that morphological changes may occur in order to conserve behaviour in the face of other changes to the cerebellum.
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Boyle, Joseph Edward. "Becoming Vegetarian: An Analysis of the Vegetarian Career Using an Integrated Model of Deviance." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/27476.
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This dissertation attempts to explore the nature of a particular food consumption pattern using a number of different deviance theories in order to outline the career path of vegetarianism. Using semi-structured interviews with 45 practicing vegetarians from two regions of the United States, the career path of the vegetarians was developed around David Matzaâ s (1969) theory of becoming deviant. Within each stage of Matzaâ s classic work, more specific theories were applied to explain the friction between vegetarianism and the more socially-accepted practice of meat eating within the United States. The framework of the stages includes the affinity for, affiliation with, and signification of vegetarian ideology and practice. Each stage within the theory is also a stage in the development of the vegetarian identity. The more specific theories utilized to explain phenomena within each particular stage attempt to show a progression from initially being interested in the ideals and practice of vegetarianism to becoming and verbalizing as a mature, practicing vegetarian. Finally, the vegetarians interviewed were asked to give the prognosis for the future of vegetarianism.Ph. D.
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Saers, Markus. "Translation as Linear Transduction : Models and Algorithms for Efficient Learning in Statistical Machine Translation." Doctoral thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-135704.
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Automatic translation has seen tremendous progress in recent years, mainly thanks to statistical methods applied to large parallel corpora. Transductions represent a principled approach to modeling translation, but existing transduction classes are either not expressive enough to capture structural regularities between natural languages or too complex to support efficient statistical induction on a large scale. A common approach is to severely prune search over a relatively unrestricted space of transduction grammars. These restrictions are often applied at different stages in a pipeline, with the obvious drawback of committing to irrevocable decisions that should not have been made. In this thesis we will instead restrict the space of transduction grammars to a space that is less expressive, but can be efficiently searched. First, the class of linear transductions is defined and characterized. They are generated by linear transduction grammars, which represent the natural bilingual case of linear grammars, as well as the natural linear case of inversion transduction grammars (and higher order syntax-directed transduction grammars). They are recognized by zipper finite-state transducers, which are equivalent to finite-state automata with four tapes. By allowing this extra dimensionality, linear transductions can represent alignments that finite-state transductions cannot, and by keeping the mechanism free of auxiliary storage, they become much more efficient than inversion transductions. Secondly, we present an algorithm for parsing with linear transduction grammars that allows pruning. The pruning scheme imposes no restrictions a priori, but guides the search to potentially interesting parts of the search space in an informed and dynamic way. Being able to parse efficiently allows learning of stochastic linear transduction grammars through expectation maximization. All the above work would be for naught if linear transductions were too poor a reflection of the actual transduction between natural languages. We test this empirically by building systems based on the alignments imposed by the learned grammars. The conclusion is that stochastic linear inversion transduction grammars learned from observed data stand up well to the state of the art.
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Wang, Ni. "Statistical Learning in Logistics and Manufacturing Systems." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11457.
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This thesis focuses on the developing of statistical methodology in reliability and quality engineering, and to assist the decision-makings at enterprise level, process level, and product level.
In Chapter II, we propose a multi-level statistical modeling strategy to characterize data from spatial logistics systems. The model can support business decisions at different levels. The information available from higher hierarchies is incorporated into the multi-level model as constraint functions for lower hierarchies. The key contributions include proposing the top-down multi-level spatial models which improve the estimation accuracy at lower levels; applying the spatial smoothing techniques to solve facility location problems in logistics.
In Chapter III, we propose methods for modeling system service reliability in a supply chain, which may be disrupted by uncertain contingent events. This chapter applies an approximation technique for developing first-cut reliability analysis models. The approximation relies on multi-level spatial models to characterize patterns of store locations and demands. The key contributions in this chapter are to bring statistical spatial modeling techniques to approximate store location and demand data, and to build system reliability models entertaining various scenarios of DC location designs and DC capacity constraints.
Chapter IV investigates the power law process, which has proved to be a useful tool in characterizing the failure process of repairable systems. This chapter presents a procedure for detecting and estimating a mixture of conforming and nonconforming systems. The key contributions in this chapter are to investigate the property of parameter estimation in mixture repair processes, and to propose an effective way to screen out nonconforming products.
The key contributions in Chapter V are to propose a new method to analyze heavily censored accelerated life testing data, and to study the asymptotic properties. This approach flexibly and rigorously incorporates distribution assumptions and regression structures into estimating equations in a nonparametric estimation framework. Derivations of asymptotic properties of the proposed method provide an opportunity to compare its estimation quality to commonly used parametric MLE methods in the situation of mis-specified regression models.
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Myers, James William. "Stochastic algorithms for learning with incomplete data an application to Bayesian networks /." Full text available online (restricted access), 1999. http://images.lib.monash.edu.au/ts/theses/Myers.pdf.
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Whalen, Andrew. "Computational, experimental, and statistical analyses of social learning in humans and animals." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/8822.
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Social learning is ubiquitous among animals and humans and is thought to be critical to the widespread success of humans and to the development and evolution of human culture. Evolutionary theory, however, suggests that social learning alone may not be adaptive but that individuals may need to be selective in who and how they copy others. One of the key findings of these evolutionary models (reviewed in Chapter 1) is that social information may be widely adaptive if individuals are able to combine social and asocial sources of information together strategically. However, up until this point the focus of theoretic models has been on the population level consequences of different social learning strategies, and not on how individuals combine social and asocial information on specific tasks. In Chapter 2 I carry out an analysis of how animal learners might incorporate social information into a reinforcement learning framework and find that even limited, low-fidelity copying of actions in an action sequence may combine with asocial learning to result in high fidelity transmission of entire action sequences. In Chapter 3 I describe a series of experiments that find that human learners flexibly use a conformity biased learning strategy to learn from multiple demonstrators depending on demonstrator accuracy, either indicated by environmental cues or past experience with these demonstrators. The chapter reveals close quantitative and qualitative matches between participant's performance and a Bayesian model of social learning. In both Chapters 2 and 3 I find, consistent with previous evolutionary findings, that by combining social and asocial sources of information together individuals are able to learn about the world effectively. Exploring how animals use social learning experimentally can be a substantially more difficult task than exploring human social learning. In Chapter 4, I develop and present a refined version of Network Based Diffusion analysis to provide a statistical framework for inferring social learning mechanisms from animal diffusion experiments. In Chapter 5 I move from examining the effects of social learning at an individual level to examining their population level outcomes and provide an analysis of how fine-grained population structure may alter the spread of novel behaviours through a population. I find that although a learner's social learning strategy and the learnability of a novel behaviour strongly impact how likely the behaviour is to spread through the population, fine grained population structure plays a much smaller role. In Chapter 6 I summarize the results of this thesis, and provide suggestions for future work to understand how individuals, humans and other animals alike, use social information.
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Liu, Chong. "Reinforcement learning with time perception." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/reinforcement-learning-with-time-perception(a03580bd-2dd6-4172-a061-90e8ac3022b8).html.
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Classical value estimation reinforcement learning algorithms do not perform very well in dynamic environments. On the other hand, the reinforcement learning of animals is quite flexible: they can adapt to dynamic environments very quickly and deal with noisy inputs very effectively. One feature that may contribute to animals' good performance in dynamic environments is that they learn and perceive the time to reward. In this research, we attempt to learn and perceive the time to reward and explore situations where the learned time information can be used to improve the performance of the learning agent in dynamic environments. The type of dynamic environments that we are interested in is that type of switching environment which stays the same for a long time, then changes abruptly, and then holds for a long time before another change. The type of dynamics that we mainly focus on is the time to reward, though we also extend the ideas to learning and perceiving other criteria of optimality, e.g. the discounted return, so that they can still work even when the amount of reward may also change. Specifically, both the mean and variance of the time to reward are learned and then used to detect changes in the environment and to decide whether the agent should give up a suboptimal action. When a change in the environment is detected, the learning agent responds specifically to the change in order to recover quickly from it. When it is found that the current action is still worse than the optimal one, the agent gives up this time's exploration of the action and then remakes its decision in order to avoid longer than necessary exploration. The results of our experiments using two real-world problems show that they have effectively sped up learning, reduced the time taken to recover from environmental changes, and improved the performance of the agent after the learning converges in most of the test cases compared with classical value estimation reinforcement learning algorithms. In addition, we have successfully used spiking neurons to implement various phenomena of classical conditioning, the simplest form of animal reinforcement learning in dynamic environments, and also pointed out a possible implementation of instrumental conditioning and general reinforcement learning using similar models.
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Gu, Tianyu. "Shelang : An Implementation of Probabilistic Programming Language and its Applications." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-26016.
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Nowadays, probabilistic models are playing a significant role in various areas in- cluding machine learning, artificial intelligence and cognitive science, etc. How- ever, as those models are becoming more and more complex, it shows that the corresponding programs are really hard to maintain and reuse as well. Meanwhile, the current tools are not feasible enough to enable probabilistic modeling and ma- chine learning to be accessible to the working programmer, who has sufficient do- main expertise, but perhaps not enough expertise in probability theory or machine learning. Probabilistic programming is one possible way to solve this. Indeed, probabilistic programming languages are powerful tools to specify probabilistic models directly in terms of a computer programs. While programmers writes normal procedures, everything will be automatically translated into statistical distributions and then users can do inferences upon them. This project aims at exploring and implementing a probabilistic programming language, for which we name as Shelang. We use Scheme, a dialect of Lisp lan- guage which is originated from λ-Calculus, to implement a embedded probabilis- tic programming language. This paper mainly discusses about the design, algo- rithms, details of this implementation and several usages of Shelang and make a conclusion in the end.
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Fuglesang, Rutger. "Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279847.
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This thesis investigates the possibility of using Sequential Monte Carlo methods (SMC) to create an online algorithm to infer properties from a dataset, such as unknown model parameters. Statistical inference from data streams tends to be difficult, and this is particularly the case for parametric models, which will be the focus of this paper. We develop a sequential Monte Carlo algorithm sampling sequentially from the model's posterior distributions. As a key ingredient of this approach, unknown static parameters are jittered towards the shrinking support of the posterior on the basis of an artificial Markovian dynamic allowing for correct pseudo-marginalisation of the target distributions. We then test the algorithm on a simple Gaussian model, a Gausian Mixture Model (GMM), as well as a variable dimension GMM. All tests and coding were done using Matlab. The outcome of the simulation is promising, but more extensive comparisons to other online algorithms for static parameter models are needed to really gauge the computational efficiency of the developed algorithm.Detta examensarbete undersöker möjligheten att använda Sekventiella Monte Carlo metoder (SMC) för att utveckla en algoritm med syfte att utvinna parametrar i realtid givet en okänd modell. Då statistisk slutledning från dataströmmar medför svårigheter, särskilt i parameter-modeller, kommer arbetets fokus ligga i utvecklandet av en Monte Carlo algoritm vars uppgift är att sekvensiellt nyttja modellens posteriori fördelningar. Resultatet är att okända, statistiska parametrar kommer att förflyttas mot det krympande stödet av posterioren med hjälp utav en artificiell Markov dynamik, vilket tillåter en korrekt pseudo-marginalisering utav mål-distributionen. Algoritmen kommer sedan att testas på en enkel Gaussisk-modell, en Gaussisk mixturmodell (GMM) och till sist en GMM vars dimension är okänd. Kodningen i detta projekt har utförts i Matlab.
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Forster, Julia. "Bridging the gap : using therapeutic models of psychology to develop Further Education teachers' strategies for promoting a culture of learning." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/362043/.
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Programmes provide a useful foundation for managing the classroom, many of the curriculum theories and approaches often appear too linear and inadequate for preparing teachers to manage the complex emotions and behaviours that their students may present on a day-to-day basis. This thesis investigates these claims and suggests that whilst effective teaching strategies can influence classroom behaviour a teacher’s ability to cultivate a culture of learning necessitates that they have a sensitive awareness of their students and an ability to positively regulate their own emotions and behaviours. Whilst it may be assumed that teachers will already have these intuitive abilities, personal experience and research highlights that this cannot be guaranteed. Reflecting on past experience as a nurse therapist in Cognitive Behaviour Therapy (CBT) and drawing on the findings of empirical research studies, it is suggested that teacher education can learn a great deal from the world of therapy. In developing this area of research at a practical level this thesis reports on a small-scale action-based project that involved designing and trialling a Cognitive Behavioural Toolkit with groups of teachers in the second year of an initial teacher education programme. Data was collected through questionnaires, personal diary entries, interviews and classroom observations. The findings of this research suggest that the majority of teachers in the sample found CBT useful for regulating their thoughts, increasing their self-confidence and improving relationships with their students. Although the results of this small-scale study cannot be generalised to other teacher education programmes, it is suggested that it provides a foundation for supporting teachers to bridge the gaps which currently exist between the curriculum theories of behaviour management and the realities of classroom practice. At the time of writing no other studies have investigated this particular topic, hence there is no comparative data to validate these claims, and so this is a notable area for further research.
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Cai, Zhiyuan. "Global Mohorovicic Discontinuity Estimates Based on Isostatic Theories Using Gravity Data and Seismic Models." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159455139426099.
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Hu, Xu. "Towards efficient learning of graphical models and neural networks with variational techniques." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1037.
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Dans cette thèse, je me concentrerai principalement sur l’inférence variationnelle et les modèles probabilistes. En particulier, je couvrirai plusieurs projets sur lesquels j'ai travaillé pendant ma thèse sur l'amélioration de l'efficacité des systèmes AI / ML avec des techniques variationnelles. La thèse comprend deux parties. Dans la première partie, l’efficacité des modèles probabilistes graphiques est étudiée. Dans la deuxième partie, plusieurs problèmes d’apprentissage des réseaux de neurones profonds sont examinés, qui sont liés à l’efficacité énergétique ou à l’efficacité des échantillonsIn this thesis, I will mainly focus on variational inference and probabilistic models. In particular, I will cover several projects I have been working on during my PhD about improving the efficiency of AI/ML systems with variational techniques. The thesis consists of two parts. In the first part, the computational efficiency of probabilistic graphical models is studied. In the second part, several problems of learning deep neural networks are investigated, which are related to either energy efficiency or sample efficiency
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Rumantir, Grace Widjaja. "Minimum message length criterion for second-order polynomial model selection applied to tropical cyclone intensity forecasting." Monash University, School of Computer Science and Software Engineering, 2003. http://arrow.monash.edu.au/hdl/1959.1/5813.
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Carstensen, Anna-Karin. "Connect : Modelling Learning to Facilitate Linking Models and the Real World trough Lab-Work in Electric Circuit Courses for Engineering Students." Doctoral thesis, Linköpings universitet, Fysik och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97395.
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A recurring question in science and engineering education is why the students do not link knowledge from theoretical classes to the real world met in laboratory courses. Mathematical models and visualisations are widely used in engineering and engineering education. Very often it is assumed that the students are familiar with the mathematical concepts used. These may be concepts taught in high school or at university level. One problem, though, is that many students have never or seldom applied their mathematical skills in other subjects, and it may be difficult for them to use their skills in a new context. Some concepts also seem to be "too difficult" to understand. One of these mathematical tools is to use Laplace Transforms to solve differential equations, and to use the derived functions to visualise transient responses in electric circuits, or control engineering. In many engineering programs at college level the application of the Laplace Transform is considered too difficult for the students to understand, but is it really, or does it depend on the teaching methods used? When applying mathematical concepts during lab work, and not teaching the mathematics and practical work in different sessions, and also using examples varied in a very systematic way, our research shows that the students approach the problem in a very different way. It shows that by developing tasks consequently according to the Theory of Variation, it is not impossible to apply the Laplace Transform already in the first year of an engineering program. The original aim of this thesis was to show: how students work with lab-tasks, especially concerning the goal to link theory to the real world how it is possible to change the ways students approach the task and thus their learning, by systematic changes in the lab-instructions During the spring 2002 students were video-recorded while working with labs in Electric Circuits. Their activity was analysed. Special focus was on what questions the students raised, and in what ways these questions were answered, and in what ways the answers were used in the further activities. This work informed the model ”learning of a complex concept”, which was used as well to analyse what students do during lab-work, and what teachers intend their students to learn. The model made it possible to see what changes in the lab-instructions that would facilitate students learning of the whole, to link theoretical models to the real world, through the labactivities. The aim of the thesis has thus become to develop a model: The learning of a complex concept show how this model can be used as well for analysis of the intended object of learning as students activities during lab-work, and thus the lived object of learning use the model in analysis of what changes in instruction that are critical for student learning. The model was used to change the instructions. The teacher interventions were included into the instructions in a systematic way, according to as well what questions that were raised by the students, as what questions that were not noticed, but expected by the teachers, as a means to form relations between theoretical aspects and measurement results. Also, problem solving sessions have been integrated into the lab sessions. Video recordings were also conducted during the spring 2003, when the new instructions were used. The students' activities were again analysed. A special focus of the thesis concerns the differences between the results from 2002 and 2003. The results are presented in four sections: Analysis of the students' questions and the teachers' answers during the lab-course 2002 Analysis of the links students need to make, the critical links for learning Analysis of the task structure before and after changes Analysis of the students' activities during the new course The thesis ends with a discussion of the conclusions which may be drawn about the possibilities to model and develop teaching sequences through research, especially concerning the aim to link theoretical models to the real world.En stående fråga som lärare i naturvetenskapliga och tekniska utbildningar ställer är varför elever och studenter inte kopplar samman kunskaper från teoretiska kursmoment med den verklighet som möts vid laborationerna. Ett vanligt syfte med laborationer är att åstadkomma länkar mellan teori och verklighet, men dessa uteblir ofta. Många gånger används avancerade matematiska modeller och grafiska representationer, vilka studenterna lärt sig i tidigare kurser, men de har sällan eller aldrig tillämpat dessa kunskaper i andra ämnen. En av dessa matematiska hjälpmedel är Laplacetransformen, som främst används för att lösa differentialekvationer, och åskådliggöra transienta förlopp i ellära eller reglerteknik. På många universitet anses Laplacetransformen numera för svår för studenterna på kortare ingenjörsutbildningar, och kurser eller kursmoment som kräver denna har strukits ut utbildningsplanerna. Men, är det för svårt, eller beror det bara på hur man presenterar Laplacetransformen? Genom att låta studenterna arbeta parallellt med matematiken och de laborativa momenten, under kombinerade lab-lektionspass, och inte vid separata lektioner och laborationer, samt genom att variera övningsexemplen på ett mycket systematiskt sätt, enligt variationsteorin, visar vår forskning att studenterna arbetar med uppgifterna på ett helt annat sätt än tidigare. Det visar sig inte längre vara omöjligt att tillämpa Laplacetransformen redan under första året på civilingenjörsutbildning inom elektroteknik. Ursprungliga syftet med avhandlingen var att visa hur studenter arbetar med laborationsuppgifter, speciellt i relation till målet att länka samman teori och verklighet hur man kan förändra studenternas aktivitet, och därmed studenternas lärande, genom att förändra laborationsinstruktionen på ett systematiskt sätt. Under våren 2002 videofilmades studenter som utförde laborationer i en kurs i elkretsteori. Deras aktivitet analyserades. Speciellt studerades vilka frågor studenterna ställde till lärarna, på vilket sätt dessa frågor besvarades, och på vilket sätt svaren användes i den fortsatta aktiviteten. Detta ledde fram till en modell för lärande av sammansatta begrepp, som kunde användas både för att analysera vad studenterna gör och vad lärarna förväntar sig att studenterna ska lära sig. Med hjälp av modellen blev det då möjligt att se vad som behövde ändra i instruktionerna för att studenterna lättare skulle kunna utföra de aktiviteter som krävs för att länka teori och verklighet. Syftet med avhandlingen är därmed att ta fram en modell för lärande av ett sammansatt begrepp visa hur denna modell kan användas för såväl analys av önskat lärandeobjekt, som av studenternas aktivitet under laborationer, och därmed det upplevda lärandeobjektet använda modellen för att analysera vilka förändringar som är kritiska för studenters lärande. Modellen användes för att förändra laborationsinstruktionerna. Lärarinterventionerna inkluderades i instruktionerna på ett systematiskt sätt utifrån dels vilka frågor som ställdes av studenterna, dels vilka frågor studenterna inte noterade, men som lärarna velat att studenterna skulle använda för att skapa relationer framför allt mellan teoretiska aspekter och mätresultat. Dessutom integrerades räkneövningar och laborationer. Videoinspelningar utfördes även våren 2003, då de nya instruktionerna användes. Även dessa analyserades med avseende på studenternas aktiviteter. Skillnader mellan resultaten från 2002 och 2003 står i fokus. Avhandlingens resultatdel består av: Analys av studenternas frågor och lärarnas svar under labkursen 2002 Analys av de länkar studenterna behöver skapa för att lära Analys av laborationsinstruktionerna före och efter förändringarna Analys av den laborationsaktivitet som blev resultatet av de nya instruktionerna, och vilket lärande som då blev möjligt Avhandlingen avlutas med en diskussion om de slutsatser som kan dras angående möjligheter att via forskning utveckla modeller av undervisningssekvenser för lärande där målet är att länka samman teori och verklighet
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Wenestam, Arvid. "Labelling factual information in legal cases using fine-tuned BERT models." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447230.
Full textAbstract:
Labelling factual information on the token level in legal cases requires legal expertise and is time-consuming. This thesis proposes transfer-learning and fine-tuning implementation of pre-trained state-of-the-art BERT models to perform this labelling task. Investigations are done to compare whether models pre-trained on solely legal corpus outperforms a generic corps trained BERT and the model’s behaviour as the number of cases in the training sample varies. This work showed that the models metric scores are stable and on par using 40-60 professionally annotated cases as opposed to using the full sample of 100 cases. Also, the generic-trained BERT model is a strong baseline, and a solely pre-trained BERT on legal corpus is not crucial for this task.
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Malvezzi, William Roberto. "Uma ferramenta baseada em teoria Fuzzy para o acompanhamento de alunos aplicado ao modelo de educação presencial mediado por tecnologia." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-21102010-162728/.
Full textAbstract:
Neste trabalho se desenvolveu uma ferramenta que auxilia na obtenção de um acompanhamento e avaliação do aprendizado dos alunos de cursos do sistema presencial mediado oferecidos pela Universidade do Estado do Amazonas (UEA), permitindo aos docentes dos cursos tomar as decisões metodológicas pedagógicas necessárias visando o bom desempenho e o aproveitamento positivo do aluno na disciplina. A ferramenta possibilita a obtenção de médias fuzificadas durante as três semanas de aula, uma a cada semana. A teoria Fuzzy foi o modelo adotado devido a vaguidade das informações advindas do ambiente de aprendizagem. Os dados coletados possuem duas fontes distintas, uma se origina no juízo de valor atribuído às atividades realizadas no ambiente virtual de aprendizagem denominado TADS (Tecnologia em Análise e Desenvolvimento de Sistemas) Virtual, a outra fonte são as avaliações escritas realizadas ao final de cada semana pelos estudantes matriculados no curso.This study has developed a solution that assists in obtaining a monitoring and evaluation of student learning in courses of the face mediated system offered by the University of the State of Amazonas (UEA), allowing both teachers and the coordination of educational courses can to make pedagogical methodology decisions aiming at the good performance of the teacher in discipline and a positive use of students. Fuzzy theory was the model adopted due to vagueness of the information from the learning environment. The collected data have two distinct sources, one originates in the value of judgments assigned to the activities performed in the virtual learning environment called TADS (Technology Assessment and Systems Development) Virtual, and the other source is the written evaluation performed at the end of each week by students enrolled in the course.
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Söderdahl, Fabian. "A Cross-Validation Approach to Knowledge Transfer for SVM Models in the Learning Using Privileged Information Paradigm." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385378.
Full textAbstract:
The learning using privileged information paradigm has allowed support vector machine models to incorporate privileged information, variables available in the training set but not in the test set, to improve predictive ability. The consequent introduction of the knowledge transfer method has enabled a practical application of support vector machine models utilizing privileged information. This thesis describes a modified knowledge transfer method inspired by cross-validation, which unlike the current standard knowledge transfer method does not create the knowledge transfer function and the approximated privileged features used in the support vector machines on the same observations. The modified method, the robust knowledge transfer, is described and evaluated versus the standard knowledge transfer method and is shown to be able to improve the predictive performance of the support vector machines for both binary classification and regression.
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Damour, Gabriel. "Information-Theoretic Framework for Network Anomaly Detection: Enabling online application of statistical learning models to high-speed traffic." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252560.
Full textAbstract:
With the current proliferation of cyber attacks, safeguarding internet facing assets from network intrusions, is becoming a vital task in our increasingly digitalised economies. Although recent successes of machine learning (ML) models bode the dawn of a new generation of intrusion detection systems (IDS); current solutions struggle to implement these in an efficient manner, leaving many IDSs to rely on rule-based techniques. In this paper we begin by reviewing the different approaches to feature construction and attack source identification employed in such applications. We refer to these steps as the framework within which models are implemented, and use it as a prism through which we can identify the challenges different solutions face, when applied in modern network traffic conditions. Specifically, we discuss how the most popular framework -- the so called flow-based approach -- suffers from significant overhead being introduced by its resource heavy pre-processing step. To address these issues, we propose the Information Theoretic Framework for Network Anomaly Detection (ITF-NAD); whose purpose is to facilitate online application of statistical learning models onto high-speed network links, as well as provide a method of identifying the sources of traffic anomalies. Its development was inspired by previous work on information theoretic-based anomaly and outlier detection, and employs modern techniques of entropy estimation over data streams. Furthermore, a case study of the framework's detection performance over 5 different types of Denial of Service (DoS) attacks is undertaken, in order to illustrate its potential use for intrusion detection and mitigation. The case study resulted in state-of-the-art performance for time-anomaly detection of single source as well as distributed attacks, and show promising results regarding its ability to identify underlying sources.I takt med att antalet cyberattacker växer snabbt blir det alltmer viktigt för våra digitaliserade ekonomier att skydda uppkopplade verksamheter från nätverksintrång. Maskininlärning (ML) porträtteras som ett kraftfullt alternativ till konventionella regelbaserade lösningar och dess anmärkningsvärda framgångar bådar för en ny generation detekteringssytem mot intrång (IDS). Trots denna utveckling, bygger många IDS:er fortfarande på signaturbaserade metoder, vilket förklaras av de stora svagheter som präglar många ML-baserade lösningar. I detta arbete utgår vi från en granskning av nuvarande forskning kring tillämpningen av ML för intrångsdetektering, med fokus på de nödvändiga steg som omger modellernas implementation inom IDS. Genom att sätta upp ett ramverk för hur variabler konstrueras och identifiering av attackkällor (ASI) utförs i olika lösningar, kan vi identifiera de flaskhalsar och begränsningar som förhindrar deras praktiska implementation. Särskild vikt läggs vid analysen av de populära flödesbaserade modellerna, vars resurskrävande bearbetning av rådata leder till signifikant tidsfördröjning, vilket omöjliggör deras användning i realtidssystem. För att bemöta dessa svagheter föreslår vi ett nytt ramverk -- det informationsteoretiska ramverket för detektering av nätverksanomalier (ITF-NAD) -- vars syfte är att möjliggöra direktanslutning av ML-modeller över nätverkslänkar med höghastighetstrafik, samt tillhandahåller en metod för identifiering av de bakomliggande källorna till attacken. Ramverket bygger på modern entropiestimeringsteknik, designad för att tillämpas över dataströmmar, samt en ASI-metod inspirerad av entropibaserad detektering av avvikande punkter i kategoriska rum. Utöver detta presenteras en studie av ramverkets prestanda över verklig internettrafik, vilken innehåller 5 olika typer av överbelastningsattacker (DoS) genererad från populära DDoS-verktyg, vilket i sin tur illustrerar ramverkets användning med en enkel semi-övervakad ML-modell. Resultaten visar på hög nivå av noggrannhet för detektion av samtliga attacktyper samt lovande prestanda gällande ramverkets förmåga att identifiera de bakomliggande aktörerna.