Dissertations / Theses on the topic 'Active learning'
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Hsu, Daniel Joseph. "Algorithms for active learning." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3404377.
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Thesis (Ph. D.)--University of California, San Diego, 2010.Title from first page of PDF file (viewed June 10, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 97-101).
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Simpson, Colin Gordon. "Exploring Chinese business management students' experience of active learning pedagogies : how much action is possible in active learning classrooms?" Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/14660.
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This phenomenological study explores how certain “innovative” pedagogies were experienced by a group of Chinese students studying Business Management at a mid-ranking UK university. Analysis of the transcripts of interviews (some in Chinese) with 24 students using NVivo shows that whilst most students felt that Active Learning pedagogies effectively supported their learning, for some students the “zone of indeterminacy” in which group projects and simulations were carried out was an uncomfortable space. Salient aspects of these students’ experiences were language, relationships and metacognitive skills, and the discussion explores the way in which these three experiential themes can be conceptualised as interrelated elements of the action (Biesta, 2006) which takes place in Active Learning classrooms. The following recommendations are made: HEIs should attempt to provide students with the advanced skills of negotiation which they will need to use in the flexible, ill-structured environments associated with Active Learning pedagogies; tutors should develop consistent approaches to collaborative assignments focussing on group work processes as well as task completion; the development of metacognitive skills through Active Learning pedagogies should be promoted through the use of explicit reflective elements embedded within the teaching, learning and assessment activities. The concluding discussion proposes that the successful use of Active Learning pedagogies requires a reconceptualisation of the purpose of education and that these pedagogies provide a potential readjustment of the balance between the functions of qualification, socialisation and subjectification (Biesta, 2010).
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Brinker, Klaus. "Active learning with kernel machines." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974403946.
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Ribeiro, de Mello Carlos Eduardo. "Active Learning : an unbiased approach." Phd thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://tel.archives-ouvertes.fr/tel-01000266.
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Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.
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Zhao, Liyue. "Active Learning with Unreliable Annotations." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5893.
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With the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to provide a large quantity of labels but controlling the quality of labels is hard. Active learning, which requires experts to annotate a subset of the most informative or uncertain data, is very sensitive to the annotation errors. Though these two techniques can be used independently of one another, by using them in combination they can complement each other's weakness. In this thesis, I investigate the development of active learning Support Vector Machines (SVMs) and expand this model to sequential data. Then I discuss the weakness of combining active learning and crowdsourcing, since the active learning is very sensitive to low quality annotations which are unavoidable for labels collected from crowdsourcing. In this thesis, I propose three possible strategies, incremental relabeling, importance-weighted label prediction and active Bayesian Networks. The incremental relabeling strategy requires workers to devote more annotations to uncertain samples, compared to majority voting which allocates different samples the same number of labels. Importance-weighted label prediction employs an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. An active learning version of Bayesian Networks is used to model the difficulty of samples and the expertise of workers simultaneously to evaluate the relative weight of workers' labels during the active learning process. All three strategies apply different techniques with the same expectation -- identifying the optimal solution for applying an active learning model with mixed label quality to crowdsourced data. However, the active Bayesian Networks model, which is the core element of this thesis, provides additional benefits by estimating the expertise of workers during the training phase. As an example application, I also demonstrate the utility of crowdsourcing for human activity recognition problems.Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
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Ganti, Mahapatruni Ravi Sastry. "New formulations for active learning." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51801.
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In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework for active learning in the pool setting, and instantiate this framework by using ideas from learning with experts, stochastic optimization, and multi-armed bandits. For the problem of learning convex combination of a given set of hypothesis, we provide a stochastic mirror descent based active learning algorithm in the stream setting.
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Huddy, Vyvyan. "Active processing in implicit learning." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390696.
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Williams, Kevin. "Active Learning for drug discovery." Thesis, Aberystwyth University, 2014. http://hdl.handle.net/2160/eaf6e66e-17fe-41a9-ac1d-9939abbb8331.
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This thesis describes work conducted to enable Robot Scientist Eve to autonomously evaluate drug-like chemicals during high throughput experiments. Eve tests libraries of chemical compounds against yeast-based targets expressing parasite and host (human) proteins (i.e. DHFR, NMT & PGK); the parasites included in this study are responsible for an array of neglected tropical diseases. The raw data for yeast growth curves from an initial screen were evaluated, and decision tree rules were constructed to describe the relative activity and toxicity of compounds. These rules were verified, and versions were subsequently developed for application to routine mass and confirmation screens. Consequently, many potential lead drug-like candidates have been identified in the Maybridge Hitfinder library; several compounds from an approved drug library (the Johns Hopkins Clinical Compound Library) have also been confirmed as exhibiting activity against these yeast-based targets. Further in vivo study of some JHCCL compounds is in progress using extracted parasite proteins; preliminary results indicate the potential for repositioning Triclosan and Tnp-470 as having anti-malarial behaviour based on their interaction with Plasmodium sp. DHFR proteins. In the second phase of the programme, a prototype Active Learning strategy was applied (active k-optimisation) to partial mass screen data as a seed; this allowed Eve to select compounds by assessing and predicting quantitative structure activity relationships (QSAR) between seed and unknown compounds. Simulations of learning and testing QSAR cycles showed that Eve would be able to select active compounds more efficiently under such a regime. Other strategies have been developed that further improve selection efficiency for active compounds, and also promote the ability to find rare category compounds. An econometric model has been developed to demonstrate the potential beneficial impact of Active Learning strategies on the execution costs for such screens.
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Mohamad, Saad. "Active learning for data streams." Thesis, Bournemouth University, 2017. http://eprints.bournemouth.ac.uk/29901/.
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With the exponential growth of data amount and sources, access to large collections of data has become easier and cheaper. However, data is generally unlabelled and labels are often difficult, expensive, and time consuming to obtain. Two learning paradigms have been used by machine learning community to diminish the need for labels in training data: semi-supervised learning (SSL) and active learning (AL). AL is a reliable way to efficiently building up training sets with minimal supervision. By querying the class (label) of the most interesting samples based upon previously seen data and some selection criteria, AL can produce a nearly optimal hypothesis, while requiring the minimum possible quantity of labelled data. SSL, on the other hand, takes the advantage of both labelled and unlabelled data to address the challenge of learning from a small number of labelled samples and large amount of unlabelled data. In this thesis, we borrow the concept of SSL by allowing AL algorithms to make use of redundant unlabelled data so that both labelled and unlabelled data are used in their querying criteria. Another common tradition within the AL community is to assume that data samples are already gathered in a pool and AL has the luxury to exhaustively search in that pool for the samples worth labelling. In this thesis, we go beyond that by applying AL to data streams. In a stream, data may grow infinitely making its storage prior to processing impractical. Due to its dynamic nature, the underlying distribution of the data stream may change over time resulting in the so-called concept drift or possibly emergence and fading of classes, known as concept evolution. Another challenge associated with AL, in general, is the sampling bias where the sampled training set does not reflect on the underlying data distribution. In presence of concept drift, sampling bias is more likely to occur as the training set needs to represent the underlying distribution of the evolving data. Given these challenges, the research questions that the thesis addresses are: can AL improve learning given that data comes in streams? Is it possible to harness AL to handle changes in streams (i.e., concept drift and concept evolution by querying selected samples)? How can sampling bias be attenuated, while maintaining AL advantages? Finally, applying AL for sequential data steams (like time series) requires new approaches especially in the presence of concept drift and concept evolution. Hence, the question is how to handle concept drift and concept evolution in sequential data online and can AL be useful in such case? In this thesis, we develop a set of stream-based AL algorithms to answer these questions in line with the aforementioned challenges. The core idea of these algorithms is to query samples that give the largest reduction of an expected loss function that measures the learning performance. Two types of AL are proposed: decision theory based AL whose losses involve the prediction error and information theory based AL whose losses involve the model parameters. Although, our work focuses on classification problems, AL algorithms for other problems such as regression and parameter estimation can be derived from the proposed AL algorithms. Several experiments have been performed in order to evaluate the performance of the proposed algorithms. The obtained results show that our algorithms outperform other state-of-the-art algorithms.
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Lima, Vinicius Gomes de. "Peer effects in active learning." reponame:Repositório Institucional do FGV, 2017. http://hdl.handle.net/10438/18273.
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This paper investigates peer effects in higher education in an environment of active learning that gives great importance for students’ interaction through group work. Our empirical strategy uses exogenous variation in group composition to estimate peer effects in different exercises. We find no evidence of peer effects in a basic linear-in-means specification considering all assigned peers. However, we find positive and statistically significant impact of peers coming from student’s same high school. We also find no evidence of peer effects with a model that takes into account student and peers’ position in the ability distribution.
Este trabalho investiga efeito de pares no ensino superior em um ambiente de active learning que dá grande importância à interação dos estudantes através do trabalho em grupo. A estratégia empírica utiliza variação exógena na composição dos grupos para estimar o efeito dos pares em diferentes exercícios. Não encontramos evidência de efeito de pares numa especificação linear-in-means básica considerando todos os pares do grupo atribuído ao aluno. Entretanto, encontramos efeito positivo e estatisticamente significante de pares que frequentaram a mesma escola de ensino médio. Não encontramos evidência de efeito de pares em um modelos que procura considerar a posição dos alunos na distribuição de habilidade.
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Monteleoni, Claire E. (Claire Elizabeth) 1975. "Learning with online constraints : shifting concepts and active learning." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38308.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 99-102).
Many practical problems such as forecasting, real-time decision making, streaming data applications, and resource-constrained learning, can be modeled as learning with online constraints. This thesis is concerned with analyzing and designing algorithms for learning under the following online constraints: i) The algorithm has only sequential, or one-at-time, access to data. ii) The time and space complexity of the algorithm must not scale with the number of observations. We analyze learning with online constraints in a variety of settings, including active learning. The active learning model is applicable to any domain in which unlabeled data is easy to come by and there exists a (potentially difficult or expensive) mechanism by which to attain labels. First, we analyze a supervised learning framework in which no statistical assumptions are made about the sequence of observations, and algorithms are evaluated based on their regret, i.e. their relative prediction loss with respect to the hindsight-optimal algorithm in a comparator class. We derive a, lower bound on regret for a class of online learning algorithms designed to track shifting concepts in this framework. We apply an algorithm we provided in previous work, that avoids this lower bound, to an energy-management problem in wireless networks, and demonstrate this application in a network simulation.
(cont.) Second, we analyze a supervised learning framework in which the observations are assumed to be iid, and algorithms are compared by the number of prediction mistakes made in reaching a target generalization error. We provide a lower bound on mistakes for Perceptron, a standard online learning algorithm, for this framework. We introduce a modification to Perceptron and show that it avoids this lower bound, and in fact attains the optimal mistake-complexity for this setting. Third, we motivate and analyze an online active learning framework. The observations are assumed to be iid, and algorithms are judged by the number of label queries to reach a target generalization error. Our lower bound applies to the active learning setting as well, as a lower bound on labels for Perceptron paired with any active learning rule. We provide a new online active learning algorithm that avoids the lower bound, and we upper bound its label-complexity. The upper bound is optimal and also bounds the algorithm's total errors (labeled and unlabeled). We analyze the algorithm further, yielding a label-complexity bound under relaxed assumptions. Using optical character recognition data, we empirically compare the new algorithm to an online active learning algorithm with data-dependent performance guarantees, as well as to the combined variants of these two algorithms.
by Claire E. Monteleoni.
Ph.D.
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Hu, François. "Semi-supervised learning in insurance : fairness and active learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG002.
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Les organismes d'assurance stockent quotidiennement des sources de données textuelles volumineuses (zones de texte libre utilisées par les téléconseillers, courriers électroniques, avis des clients, etc.). Cependant, cette masse de données textuelles comporte des enjeux spécifiques en termes de réglementations comme par exemple le respect des contraintes de protection de la vie privée, imposées en Europe par le récent Règlement général sur la protection des données (RGPD) : ces données textuelles peuvent contenir des informations non-conformes aux normes RGPD, soulevant ainsi des questions éthiques et ne peuvent pas être conservées par l'assureur. Aujourd'hui, ces données textuelles sont étiquetées par des experts (oracles) et ce processus n'est pas adapté à la gestion de grands volumes ni à une gestion de l'information en temps quasi réel. Par conséquent, la mise en place d'un système d'apprentissage précis (en termes de prédiction), peu coûteux (en termes d'étiquetage) et éthique (en termes d'équité) est nécessaire en assurance et cette thèse aborde et résout certains de ces défis. Le premier défi est de réduire l'effort d'étiquetage (se concentrant ainsi sur la qualité des données) avec l'aide de l'apprentissage actif, une boucle de rétroaction entre l'inférence du modèle et un oracle : puisqu'en assurance les données non étiquetées sont généralement abondantes, l'apprentissage actif peut devenir un atout important pour réduire le coût de l'étiquetage. Un autre défi majeur est la question de l'équité dans les inférences de modèles ML. Puisque des inégalités et des discriminations peuvent être trouvées dans les données, les modèles d'apprentissage sont susceptibles de reproduire certaines injustices, ce qui les rend inutilisables en production. Cette thèse explore ces problèmes et propose des solutions, notamment pour les tâches de classification multi-classes. En particulier, nous proposons une méthode d'équité algorithmique qui garantit soit une équité exacte au détriment de la précision du modèle, soit un compromis entre équité et précision appelé epsilon-fairness. En outre, nous proposons une méthode d'apprentissage actif équitable qui requête les instances informatives tout en rendant le modèle équitable. Les méthodologies proposées ont l'avantage d'être agnostiques par rapport au modèle d'apprentissage statistique. Ces résultats sont étudiés et appliqués sur des jeux de données réels et synthétiquesInsurance organisations store voluminous textual data sources on a daily basis (free text fields used by telephonists, emails, customer reviews, ...). However, this mass of textual data involves specific issues in terms of regulations, such as compliance with the privacy constraints imposed in Europe by the recent General Data Protection Regulation (GDPR) : this textual data may contain information that is not compliant with the RGPD standards, thus raising ethical issues and cannot be retained by the insurer. Today, this textual data is tagged by experts (oracles) and this process is not suitable for managing large volumes and near real-time information. Therefore, the implementation of an accurate (in terms of prediction), low-cost (in terms of labelling) and ethical (in terms of fairness) learning system is needed in insurance and this thesis addresses and solves some of these challenges. The first challenge is to reduce the labelling effort (thus focusing on data quality) with the help of active learning, a feedback loop between model inference and an oracle: since in insurance unlabelled data is usually abundant, active learning can become an important asset to reduce the cost of labelling. Another major challenge is the issue of fairness in Machine Learning model inferences. Since inequalities and discriminations can be found in the data, learning models are likely to reproduce some unfairness, making them unusable in production. This thesis explores these problems and proposes solutions, especially for multi-class classification tasks. In particular, we propose an algorithmic fairness method that guarantees either exact fairness at the expense of model accuracy, or a compromise between fairness and accuracy called epsilon-fairness. In addition, we propose a fair active learning method that requests informative instances while making the model fair. The proposed methodologies have the advantage of being agnostic with respect to the statistical learning model. These results are studied and applied on real and synthetic datasets
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Barnabé-Lortie, Vincent. "Active Learning for One-class Classification." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33001.
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Active learning is a common solution for reducing labeling costs and maximizing the impact of human labeling efforts in binary and multi-class classification settings. However, when we are faced with extreme levels of class imbalance, a situation in which it is not safe to assume that we have a representative sample of the minority class, it has been shown effective to replace the binary classifiers with a one-class classifiers. In such a setting, traditional active learning methods, and many previously proposed in the literature for one-class classifiers, prove to be inappropriate, as they rely on assumptions about the data that no longer stand.
In this thesis, we propose a novel approach to active learning designed for one-class classification. The proposed method does not rely on many of the inappropriate assumptions of its predecessors and leads to more robust classification performance. The gist of this method consists of labeling, in priority, the instances considered to fit the learned class the least by previous iterations of a one-class classification model.
Throughout the thesis, we provide evidence for the merits of our method, then deepen our understanding of these merits by exploring the properties of the method that allow it to outperform the alternatives.
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Muhivuwomunda, Divine. "Data De-Duplication through Active Learning." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28859.
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Data de-duplication concerns the identification and eventual elimination of records, in a particular dataset, that refer to the same entity without necessarily having the same attribute values, nor the same identifying values. Machine Learning techniques have been used to handle data de-duplication. Active Learning using ensemble learning methods is one such technique. An ensemble learning algorithm is used to create, from the same training set, a set of models that are different. Active Learning then iteratively passes unlabeled pairs of records to the created models for labeling as duplicates, or non-duplicates, and selectively picks the pairs that cause most disagreement among the models. The selected pairs of instances are considered to bring most information gain to the learning process. Active Learning thus continuously teaches a learner to find duplicate instances by providing the learner with a better training set.
This thesis evaluates how Active Learning undertakes the task of data de-duplication when Query by Bagging and Query by Boosting algorithms are used. During the evaluation, we investigate the performance of Active Learning in various situations. We study the impact of varying the data size as well as the impact of using different blocking methods, which are methods used to reduce the number of potential duplicates for comparison. We also consider the performance of Active Learning when a synthetic dataset is used versus a real-world dataset.
The experimental results show that Active Learning using Query by Bagging performs well on synthetic datasets and only requires a few iterations to generate a good de-duplication function. The size of the dataset does not seem to have much effect on the results. When the experiment is conducted on real-world data, Active Learning using Query by Bagging still performs well, except when the dataset has a significant amount of noise. However, the learning process for real world data is not as smooth compared to when the synthetic data is used. The performance using Canopy Clustering and Bigram Indexing blocking methods were evaluated and the results show better results for the Bigram Indexing.
Active Learning using Query by Boosting shows a good performance on synthetic data sets. It also generates good results on real-world data sets. However, the presence of noise in the dataset negatively affects the performance of the learning process. Again, the dataset size does not affect the performance while using Query by Boosting. The evaluation of the de-duplication function using Canopy Clustering and Bigram Indexing does not show any significant difference.
We further compare the performance results when using Query by Bagging versus Query by Boosting. First, when compare the two methods using two different blocking methods, the experiment shows that Query by Boosting yields better results for both Canopy Clustering and Bigram Indexing. When considering synthetic versus real-world data, the same observation holds.
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Schurr, Jochen. "On assortment optimization under active learning." Thesis, Lancaster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658059.
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Assortment optimization, also called assortment planning, is the decision process of a retailer of choosing a limited number of products that are to be presented to customers in a show room or an equivalent environment. Assortment optimization has been a subject of active interest in an academic setting since the 1970s and with the emergence of electronic data processing it has become a major driver of success for retail companies. In this thesis, we consider a rather new subfield there of: dynamic assortment planning. Retailers with the ability to adapt quickly to demand observations can beat the market by adapting the assortment towards the products that turn out to sell best. This creates larger revenue and at the same time reduces the costs caused by idle inventory. Having only vague knowledge of the actual demand, the task becomes bifold and an exploration exploitation type trade off between learning about the demand and utilizing this knowledge towards profit maximization has to be faced. We develop various models and appropriate, close- to-optimal, heuristic decision policies in an apparel retailing context. In a first setting, we derive heuristic policies basing on Gittins indices for multi-armed bandit models and develop heuristic methods to apply them in a non- trivial knapsack type constraint situation. Extensive numerical testing demonstrates the outstanding performance strength of our policies and we are able to derive remarkably tight upper bounds to the non- tractable optimal solution. We then extend this model to account for substitution effects, which inflict a tremendous increase in complexity on the problem. ·With the use of stronger simplifications than before, we are still able to develop heuristic policies with active learning. Numerical studies indicate an improvement towards a myopic policy in a similar order as in the previous setting. We close this study by suggesting an improvement on a well- known heuristic method.
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Johannemann, Jonathan. "COAL : a continuous active learning system." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111453.
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Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-60).
In this thesis, our objective is to enable businesses looking to enhance their product by varying its attributes, where effectiveness of the new product is assessed by humans. To achieve this, we mapped the task to a machine learning problem. The solution is two fold: learn a non linear model that can map the attribute space to the human response, which can then be used to make predictions, and an active learning strategy that enables learning this model incrementally. We developed a system called Continuous active learning system (COAL).
by Jonathan Johannemann.
M. Fin.
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Budnik, Mateusz. "Active and deep learning for multimedia." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM011.
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Les thèmes principaux abordés dans cette thèse sont l'utilisation de méthodes d'apprentissage actif et d'apprentissage profond dans le contexte du traitement de documents multimodaux. Les contributions proposées dans cette thèse abordent ces deux thèmes. Un système d'apprentissage actif a été introduit pour permettre une annotation plus efficace des émissions de télévision grâce à la propagation des étiquettes, à l'utilisation de données multimodales et à des stratégies de sélection efficaces. Plusieurs scénarios et expériences ont été envisagés dans le cadre de l'identification des personnes dans les vidéos, en prenant en compte l'utilisation de différentes modalités (telles que les visages, les segments de la parole et le texte superposé) et différentes stratégies de sélection. Le système complet a été validé au cours d'un ``test à blanc'' impliquant des annotateurs humains réels.Une deuxième contribution majeure a été l'étude et l'utilisation de l'apprentissage profond (en particulier les réseaux de neurones convolutifs) pour la recherche d'information dans les vidéos. Une étude exhaustive a été réalisée en utilisant différentes architectures de réseaux neuronaux et différentes techniques d'apprentissage telles que le réglage fin (fine-tuning) ou des classificateurs plus classiques comme les SVMs. Une comparaison a été faite entre les caractéristiques apprises (la sortie des réseaux neuronaux) et les caractéristiques plus classiques (``engineered features''). Malgré la performance inférieure des seconds, une fusion de ces deux types de caractéristiques augmente la performance globale.Enfin, l'utilisation d'un réseau neuronal convolutif pour l'identification des locuteurs à l'aide de spectrogrammes a été explorée. Les résultats ont été comparés à ceux obtenus avec d'autres systèmes d'identification de locuteurs récents. Différentes approches de fusion ont également été testées. L'approche proposée a permis d'obtenir des résultats comparables à ceux certains des autres systèmes testés et a offert une augmentation de la performance lorsqu'elle est fusionnée avec la sortie du meilleur systèmeThe main topics of this thesis include the use of active learning-based methods and deep learning in the context of retrieval of multimodal documents. The contributions proposed during this thesis address both these topics. An active learning framework was introduced, which allows for a more efficient annotation of broadcast TV videos thanks to the propagation of labels, the use of multimodal data and selection strategies. Several different scenarios and experiments were considered in the context of person identification in videos, including using different modalities (such as faces, speech segments and overlaid text) and different selection strategies. The whole system was additionally validated in a dry run involving real human annotators.A second major contribution was the investigation and use of deep learning (in particular the convolutional neural network) for video retrieval. A comprehensive study was made using different neural network architectures and training techniques such as fine-tuning or using separate classifiers like SVM. A comparison was made between learned features (the output of neural networks) and engineered features. Despite the lower performance of the engineered features, fusion between these two types of features increases overall performance.Finally, the use of convolutional neural network for speaker identification using spectrograms is explored. The results are compared to other state-of-the-art speaker identification systems. Different fusion approaches are also tested. The proposed approach obtains comparable results to some of the other tested approaches and offers an increase in performance when fused with the output of the best system
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Chernets, M. "Teaching strategies to promote active learning." Thesis, Київський національний університет технологій та дизайну, 2019. https://er.knutd.edu.ua/handle/123456789/13027.
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Дорда, Світлана Володимирівна, Светлана Владимировна Дорда, and Svitlana Volodymyrivna Dorda. "Using the WEB for active learning." Thesis, Sumy State University, 2003. http://essuir.sumdu.edu.ua/handle/123456789/62784.
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The classroom is only one place in which learning takes place and there are more autonomous ways of learning. Developments in technology increases in demand but not in resources, and research into learning are all changing the face of how, where and when to learn.
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Hříbek, David. "Active Learning pro zpracování archivních pramenů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445535.
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This work deals with the creation of a system that allows uploading and annotating scans of historical documents and subsequent active learning of models for character recognition (OCR) on available annotations (marked lines and their transcripts). The work describes the process, classifies the techniques and presents an existing system for character recognition. Above all, emphasis is placed on machine learning methods. Furthermore, the methods of active learning are explained and a method of active learning of available OCR models from annotated scans is proposed. The rest of the work deals with a system design, implementation, available datasets, evaluation of self-created OCR model and testing of the entire system.
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Weir, Jennifer Anne. "Active learning in transportation engineering education." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-12214-155616/.
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Taylor, Teresa Brooks. "Being Intentional: Active Learning, Student Reflection." Digital Commons @ East Tennessee State University, 2000. https://dc.etsu.edu/etsu-works/3645.
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Kihlström, Helena. "Active Stereo Reconstruction using Deep Learning." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158276.
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Depth estimation using stereo images is an important task in many computer vision applications. A stereo camera contains two image sensors that observe the scene from slightly different viewpoints, making it possible to find the depth of the scene. An active stereo camera also uses a laser projector that projects a pattern into the scene. The advantage of the laser pattern is the additional texture that gives better depth estimations in dark and textureless areas. Recently, deep learning methods have provided new solutions producing state-of-the-art performance in stereo reconstruction. The aim of this project was to investigate the behavior of a deep learning model for active stereo reconstruction, when using data from different cameras. The model is self-supervised, which solves the problem of having enough ground truth data for training the model. It instead uses the known relationship between the left and right images to let the model learn the best estimation. The model was separately trained on datasets from three different active stereo cameras. The three trained models were then compared using evaluation images from all three cameras. The results showed that the model did not always perform better on images from the camera that was used for collecting the training data. However, when comparing the results of different models using the same test images, the model that was trained on images from the camera used for testing gave better results in most cases.
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Susag, Angie. "Expeditionary learning." Diss., [Missoula, Mont.] : The University of Montana, 2009. http://etd.lib.umt.edu/theses/available/etd-07142009-111349.
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Gür, Hülya. "Learning to teach mathematics and the place of active learning." Thesis, University of Leicester, 1999. http://hdl.handle.net/2381/30939.
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This study is concerned with the comparison of "learning to teach' studies in teacher training programmes in Turkey and England with special reference to using active learning approaches and stage theories. It aims to realise the following two main objectives in terms of training programmes: 1. To indicate to what extent the adaptation of an active learning approach in teacher training programmes makes an impact on learning to teach. 2. To describe and compare the similarities and differences in trainees' learning to teach in both training programmes and to make connections with the broader educational policies in Turkish and English Teacher Training Programmes and in Schools. It begins with a literature review of learning to teach and active learning and then examines different aspects of the presentation of the stage theory in terms of the stages trainees go through during their teaching practices in order to reach the "reflective teaching stage'. This present study concludes with the presentation of findings and evaluation of the contribution of this research. The research design combined a qualitative approach in a quantitative framework. Two contrasting training courses were followed through their one-year programmes. Data collection was from classroom observations, examining documents (including official documents and trainees' written documents), semi-structured interview with four trainees and a mathematics subject tutor and questionnaires. English and Turkish versions of the questionnaire were developed, tested and piloted. The English questionnaire was administered (n=12) at the end of the first teaching practice and at the end of the last teaching practice. The Turkish questionnaire was administered (n=57) at the end of the first semester. The aim of conducting the questionnaires was to find out trainees' beliefs and views about teaching and to chart changes in these. In-depth study of how four trainees learn to teach in an English programme is central to the qualitative work in relation to Stage Theory and the place of Active Learning, both in classrooms and university training programmes. Given the centrality of the workplace for training, the study highlights the need to take account of each trainee's learning, in English and Turkish programmes, and to pay more attention to pedagogical content knowledge. If what is learned is influenced by how and where learning occurs, as demonstrated in the present study, then the Active Learning account of the Stage Theory may be an appropriate theoretical model for delimiting the scope of school based training, investigating the practical problems in learning to teach in the English teacher training programme, and adapting the findings to the Turkish Teacher training programme.
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Alabdulrahman, Rabaa. "A Comparative Study of Ensemble Active Learning." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31805.
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Data Stream mining is an important emerging topic in the data mining and machine learning domain. In a Data Stream setting, the data arrive continuously and often at a fast pace. Examples include credit cards transaction records, surveillances video streams, network event logs, and telecommunication records. Such types of data bring new challenges to the data mining research community. Specifically, a number of researchers have developed techniques in order to build accurate classification models against such Data Streams. Ensemble Learning, where a number of so-called base classifiers are combined in order to build a model, has shown some promise. However, a number of challenges remain. Often, the class labels of the arriving data are incorrect or missing. Furthermore, Data Stream algorithms may benefit from an online learning paradigm, where a small amount of newly arriving data is used to learn incrementally. To this end, the use of Active Learning, where the user is in the loop, has been proposed as a way to extend Ensemble Learning. Here, the hypothesis is that Active Learning would increase the performance, in terms of accuracy, ensemble size, and the time it takes to build the model.
This thesis tests the validity of this hypothesis. Namely, we explore whether augmenting Ensemble Learning with an Active Learning component benefits the Data Stream Learning process. Our analysis indicates that this hypothesis does not necessarily hold for the datasets under consideration. That is, the accuracies of Active Ensemble Learning are not statistically significantly higher than when using normal Ensemble Learning. Rather, Active Learning may even cause an increase in error rate. Further, Active Ensemble Learning actually results in an increase in the time taken to build the model. However, our results indicate that Active Ensemble Learning builds accurate models against much smaller ensemble sizes, when compared to the traditional Ensemble Learning algorithms. Further, the models we build are constructed against small and incrementally growing training sets, which may be very beneficial in a real time Data Stream setting.
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Evans, Cindy. "The Effect of Faculty Development on Active Learning in the College Classroom." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc2762/.
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This study examined the effect of active learning seminars and a mentoring program on the use of active learning teaching techniques by college faculty. A quasi-experimental study was conducted using convenience samples of faculty from two private Christian supported institutions. Data for the study were collected from surveys and faculty course evaluations. The study lasted one semester. Faculty volunteers from one institution served as the experimental group and faculty volunteers from the second institution were the comparison group. The experimental group attended approximately eight hours of active learning seminars and also participated in a one-semester mentoring program designed to assist faculty in application of active learning techniques. Several individuals conducted the active learning seminars. Dr. Charles Bonwell, a noted authority on active learning, conducted the first three-hour seminar. Seven faculty who had successfully used active learning in their classrooms were selected to conduct the remaining seminars. The faculty-mentoring program was supervised by the researcher and conducted by department chairs. Data were collected from three surveys and faculty course evaluations. The three surveys were the Faculty Active Learning Survey created by the researcher, the Teaching Goals Inventory created by Angelo and Cross, and the college edition of Learner-Centered Practices by Barbara McCombs. The use of active learning techniques by the experimental group increased significantly more than the use by those in the convenience sample. No statistical difference was found in the change of professors' teaching beliefs or the course evaluation results.
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Scott, Fiona Marie. "Action-reflection-learning in a lean production environment /." St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17167.pdf.
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Berlind, Christopher. "New insights on the power of active learning." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53948.
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Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to acquire than accompanying labels. Active learning is an extension of the classical paradigm intended to lessen the expense of the labeling process by allowing the learning algorithm to intelligently choose which examples should be labeled.
In this dissertation, we demonstrate that the power to make adaptive label queries has benefits beyond reducing labeling effort over passive learning. We develop and explore several novel methods for active learning that exemplify these new capabilities. Some of these methods use active learning for a non-standard purpose, such as computational speedup, structure discovery, and domain adaptation. Others successfully apply active learning in situations where prior results have given evidence of its ineffectiveness.
Specifically, we first give an active algorithm for learning disjunctions that is able to overcome a computational intractability present in the semi-supervised version of the same problem. This is the first known example of the computational advantages of active learning. Next, we investigate using active learning to determine structural properties (margins) of the data-generating distribution that can further improve learning rates. This is in contrast to most active learning algorithms which either assume or ignore structure rather than seeking to identify and exploit it. We then give an active nearest neighbors algorithm for domain adaptation, the task of learning a predictor for some target domain using mostly examples from a different source domain. This is the first formal analysis of the generalization and query behavior of an active domain adaptation algorithm. Finally, we show a situation where active learning can outperform passive learning on very noisy data, circumventing prior results that active learning cannot have a significant advantage over passive learning in high-noise regimes.
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Denton, Stephen E. "Exploring active learning in a Bayesian framework." [Bloomington, Ind.] : Indiana University, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3380073.
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Thesis (Ph.D.)--Indiana University, Dept. of Psychological and Brain Sciences the Dept. of Cognitive Science, 2009.Title from PDF t.p. (viewed on Jul 19, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: B, page: 7870. Advisers: John K. Kruschke; Jerome R. Busemeyer.
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Houlsby, Neil. "Efficient Bayesian active learning and matrix modelling." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/248885.
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With the advent of the Internet and growth of storage capabilities, large collections of unlabelled data are now available. However, collecting supervised labels can be costly. Active learning addresses this by selecting, sequentially, only the most useful data in light of the information collected so far. The online nature of such algorithms often necessitates efficient computations. Thus, we present a framework for information theoretic Bayesian active learning, named Bayesian Active Learning by Disagreement, that permits efficient and accurate computations of data utility. Using this framework we develop new techniques for active Gaussian process modelling and adaptive quantum tomography. The latter has been shown, in both simulation and laboratory experiments, to yield faster learning rates than any non-adaptive design. Numerous datasets can be represented as matrices. Bayesian models of matrices are becoming increasingly popular because they can handle noisy or missing elements, and are extensible to different data-types. However, efficient inference is crucial to allow these flexible probabilistic models to scale to large real-world datasets. Binary matrices are a ubiquitous datatype, so we present a stochastic inference algorithm for fast learning in this domain. Preference judgements are a common, implicit source of binary data. We present a hybrid matrix factorization/Gaussian process model for collaborative learning from multiple users' preferences. This model exploits both the structure of the matrix and can incorporate additional covariate information to make accurate predictions. We then combine matrix modelling with active learning and propose a new algorithm for cold-start learning with ordinal data, such as ratings. This algorithm couples Bayesian Active Learning by Disagreement with a heteroscedastic model to handle varying levels of noise. This ordinal matrix model is also used to analyze psychometric questionnaires; we analyze classical assumptions made in psychometrics and show that active learning methods can reduce questionnaire lengths substantially.
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Zhao, Wenquan. "Deep Active Learning for Short-Text Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212577.
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In this paper, we propose a novel active learning algorithm for short-text (Chinese) classification applied to a deep learning architecture. This topic thus belongs to a cross research area between active learning and deep learning. One of the bottlenecks of deeplearning for classification is that it relies on large number of labeled samples, which is expensive and time consuming to obtain. Active learning aims to overcome this disadvantage through asking the most useful queries in the form of unlabeled samples to belabeled. In other words, active learning intends to achieve precise classification accuracy using as few labeled samples as possible. Such ideas have been investigated in conventional machine learning algorithms, such as support vector machine (SVM) for imageclassification, and in deep neural networks, including convolutional neural networks (CNN) and deep belief networks (DBN) for image classification. Yet the research on combining active learning with recurrent neural networks (RNNs) for short-text classificationis rare. We demonstrate results for short-text classification on datasets from Zhuiyi Inc. Importantly, to achieve better classification accuracy with less computational overhead,the proposed algorithm shows large reductions in the number of labeled training samples compared to random sampling. Moreover, the proposed algorithm is a little bit better than the conventional sampling method, uncertainty sampling. The proposed activelearning algorithm dramatically decreases the amount of labeled samples without significantly influencing the test classification accuracy of the original RNNs classifier, trainedon the whole data set. In some cases, the proposed algorithm even achieves better classification accuracy than the original RNNs classifier.I detta arbete studerar vi en ny aktiv inlärningsalgoritm som appliceras på en djup inlärningsarkitektur för klassificering av korta (kinesiska) texter. Ämnesområdet hör därmedtill ett ämnesöverskridande område mellan aktiv inlärning och inlärning i djupa nätverk .En av flaskhalsarna i djupa nätverk när de används för klassificering är att de beror avtillgången på många klassificerade datapunkter. Dessa är dyra och tidskrävande att skapa. Aktiv inlärning syftar till att överkomma denna typ av nackdel genom att generera frågor rörande de mest informativa oklassade datapunkterna och få dessa klassificerade. Aktiv inlärning syftar med andra ord till att uppnå bästa klassificeringsprestanda medanvändandet av så få klassificerade datapunkter som möjligt. Denna idé har studeratsinom konventionell maskininlärning, som tex supportvektormaskinen (SVM) för bildklassificering samt inom djupa neuronnätverk inkluderande bl.a. convolutional networks(CNN) och djupa beliefnetworks (DBN) för bildklassificering. Emellertid är kombinationenav aktiv inlärning och rekurrenta nätverk (RNNs) för klassificering av korta textersällsynt. Vi demonstrerar här resultat för klassificering av korta texter ur en databas frånZhuiyi Inc. Att notera är att för att uppnå bättre klassificeringsnoggranhet med lägre beräkningsarbete (overhead) så uppvisar den föreslagna algoritmen stora minskningar i detantal klassificerade träningspunkter som behövs jämfört med användandet av slumpvisadatapunkter. Vidare, den föreslagna algoritmen är något bättre än den konventionellaurvalsmetoden, osäkherhetsurval (uncertanty sampling). Den föreslagna aktiva inlärningsalgoritmen minska dramatiskt den mängd klassificerade datapunkter utan att signifikant påverka klassificeringsnoggranheten hos den ursprungliga RNN-klassificeraren när den tränats på hela datamängden. För några fall uppnår den föreslagna algoritmen t.o.m.bättre klassificeringsnoggranhet än denna ursprungliga RNN-klassificerare.
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Cora, Vlad M. "Model-based active learning in hierarchical policies." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/737.
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Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games. Game designers find it very natural to adopt a divide-and-conquer philosophy of specifying hierarchical policies, where decision modules can be constructed somewhat independently. The process of choosing the parameters of these modules manually is typically lengthy and tedious. The hierarchical reinforcement learning (HRL) field has produced elegant ways of decomposing policies and value functions using semi-Markov decision processes. However, there is still a lack of demonstrations in larger nonlinear systems with discrete and continuous variables. To narrow this gap between industrial practices and academic ideas, we address the problem of designing efficient algorithms to facilitate the deployment of HRL ideas in more realistic settings. In particular, we propose Bayesian active learning methods to learn the relevant aspects of either policies or value functions by focusing on the most relevant parts of the parameter and state spaces respectively. To demonstrate the scalability of our solution, we have applied it to The Open Racing Car Simulator (TORCS), a 3D game engine that implements complex vehicle dynamics. The environment is a large topological map roughly based on downtown Vancouver, British Columbia. Higher level abstract tasks are also learned in this process using a model-based extension of the MAXQ algorithm. Our solution demonstrates how HRL can be scaled to large applications with complex, discrete and continuous non-linear dynamics.
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Watanabe, Yukio. "Learning control of automotive active suspension systems." Thesis, Cranfield University, 1997. http://dspace.lib.cranfield.ac.uk/handle/1826/13865.
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This thesis considers the neural network learning control of a variable-geometry automotive active suspension system which combines most of the benefits of active suspension systems with low energy consumption. Firstly, neural networks are applied to the control of various simplified automotive active suspensions, in order to understand how a neural network controller can be integrated with a physical dynamic system model. In each case considered, the controlled system has a defined objective and the minimisation of a cost function. The neural network is set up in a learning structure, such that it systematically improves the system performance via repeated trials and modifications of parameters. The learning efficiency is demonstrated by the given system performance in agreement with prior results for both linear and non-linear systems. The above simulation results are generated by MATLAB and the Neural Network Toolbox. Secondly, a half-car model, having one axle and an actuator on each side, is developed via the computer language, AUTOSIM. Each actuator varies the ratio of the spring/damper unit length change to wheel displacement in order to control each wheel rate. The neural network controller is joined with the half-car model and learns to reduce the defined cost function containing a weighted sum of the squares of the body height change, body roll and actuator displacements. The performances of the neurocontrolled system are compared with those of passive and proportional-plusdifferential controlled systems under various conditions. These involve various levels of lateral force inputs and vehicle body weight changes. Finally, energy consumption of the variable-geometry system, with either the neurocontrol or proportional-plus-differential control, is analysed using an actuator model via the computer simulation package, SIMULINK. The simulation results are compared with those of other actively-controlled suspension systems taken from the literature.
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Bheda, Anuj. "Predictive analytics of active learning based education." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113509.
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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 113-115).
Learning Analytics (LA) is defined as the collection, measurement, and analysis of data related to student performance such that the feedback from the analytical insights can be used to optimize student learning and improve student outcomes. Blended Learning (BL) is a teaching paradigm that involves a mix of face-to-face interactions in a classroom based setting along with instructional material distributed through an online medium. In this thesis, we explore the role of a blended learning model coupled with learning analytics in an introductory programming class for non-computer science students. We identify the features that were necessary for setting up the infrastructure of the course. These include discussions on preparing the course content materials and producing assignment exercises. We then talk about the various dynamics that were in play during the duration of the class by describing the interplay between watching video tutorials, listening to mini-lectures and performing active learning exercises that are backed by modern software development practices. Lastly, we spend time analyzing the data collected to create a predictive model that can measure student performance by defining the specifications of a machine learning algorithm along with many of its adjustable parameters. The system thus created will allow instructors to identify possible outliers in teaching efficacy, the feedback from which could then be used to tune course material for the betterment of student outcomes.
by Anuj Bheda.
S.M. in Engineering and Management
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Shepherdson, Emma 1972. "Teaching concepts utilizing active learning computer environments." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/84215.
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Hansson, Kim, and Erik Hörlin. "Active learning via Transduction in Regression Forests." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10935.
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Context. The amount of training data required to build accurate modelsis a common problem in machine learning. Active learning is a techniquethat tries to reduce the amount of required training data by making activechoices of which training data holds the greatest value.Objectives. This thesis aims to design, implement and evaluate the Ran-dom Forests algorithm combined with active learning that is suitable forpredictive tasks with real-value data outcomes where the amount of train-ing data is small. machine learning algorithms traditionally requires largeamounts of training data to create a general model, and training data is inmany cases sparse and expensive or difficult to create.Methods.The research methods used for this thesis is implementation andscientific experiment. An approach to active learning was implementedbased on previous work for classification type problems. The approachuses the Mahalanobis distance to perform active learning via transduction.Evaluation was done using several data sets were the decrease in predictionerror was measured over several iterations. The results of the evaluationwas then analyzed using nonparametric statistical testing.Results. The statistical analysis of the evaluation results failed to detect adifference between our approach and a non active learning approach, eventhough the proposed algorithm showed irregular performance. The evalu-ation of our tree-based traversal method, and the evaluation of the Maha-lanobis distance for transduction both showed that these methods performedbetter than Euclidean distance and complete graph traversal.Conclusions. We conclude that the proposed solution did not decreasethe amount of required training data on a significant level. However, theapproach has potential and future work could lead to a working active learn-ing solution. Further work is needed on key areas of the implementation,such as the choice of instances for active learning through transduction un-certainty as well as choice of method for going from transduction model toinduction model.
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Van, Amerom W. P. C. "Active / interactive learning facilitation in large classes." Journal for New Generation Sciences, Vol 3, Issue 2: Central University of Technology, Free State, Bloemfontein, 2005. http://hdl.handle.net/11462/483.
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Published ArticleAnyone who has taught a large class is aware of the physical and emotional constraints upon both lecturer and students. For students the dominant problems are anonymity, passivity and a frustration of not being able to say what is happening to them. For lecturers the dominant problems are not being able to relate to students as individuals, a feeling of being driven back to traditional teaching, being overwhelmed by assessment demands, and a sense of not being in control of the class. An increase in class size requires lecturers radically to reconsider how they deliver their courses. One such strategy proposed in this paper is that of active learning facilitation - getting students to work and think in the classroom about what and why they are doing what they are doing.
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Tan, Run Yan. "Active Learning using a Sample Selector Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287312.
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In this work, we set the stage of a limited labelling budget and propose using a sample selector network to learn and select effective training samples, whose labels we would then acquire to train the target model performing the required machine learning task. We make the assumption that the sample features, the state of the target model and the training loss of the target model are informative for training the sample selector network. In addition, we approximate the state of the target model with its intermediate and final network outputs. We investigate if under a limited labelling budget, the sample selector network is capable of learning and selecting training samples that train the target model at least as effectively as using another training subset of the same size that is uniformly randomly sampled from the full training dataset, the latter being the common procedure used to train machine learning models without active learning. We refer to this common procedure as the traditional machine learning uniform random sampling method. We perform experiments on the MNIST and CIFAR-10 datasets; and demonstrate with empirical evidence that under a constrained labelling budget and some other conditions, active learning using a sample selector network enables the target model to learn more effectively.I detta arbete sätter vi steget i en begränsad märkningsbudget och föreslår att vi använder ett provväljarnätverk för att lära och välja effektiva träningsprover, vars etiketter vi sedan skulle skaffa för att träna målmodellen som utför den nödvändiga maskininlärningsuppgiften. Vi antar att provfunktionerna, tillståndet för målmodellen och utbildningsförlusten för målmodellen är informativa för att träna provväljarnätverket. Dessutom uppskattar vi målmodellens tillstånd med dess mellanliggande och slutliga nätverksutgångar. Vi undersöker om provväljarnätverket enligt en begränsad märkningsbudget kan lära sig och välja utbildningsprover som tränar målmodellen minst lika effektivt som att använda en annan träningsdel av samma storlek som är enhetligt slumpmässigt samplad från hela utbildningsdatasystemet, det senare är det vanliga förfarandet som används för att utbilda maskininlärningsmodeller utan aktivt lärande. Vi hänvisar till denna vanliga procedur som den traditionella maskininlärning enhetliga slumpmässig sampling metod. Vi utför experiment på datasätten MNIST och CIFAR-10; och visa med empiriska bevis att under en begränsad märkningsbudget och vissa andra förhållanden, aktivt lärande med hjälp av ett provvalnätverk gör det möjligt för målmodellen att lära sig mer effektivt.
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Ahsan, Nasir. "Combining Exploration and Exploitation in Active Learning." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10144.
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This thesis investigates the active learning in the presence of model bias. State of the art approaches advocate combining exploration and exploitation in active learning. However, they suffer from premature exploitation or unnecessary exploration in the later stages of learning. We propose to combine exploration and exploitation in active learning by discarding instances outside a sampling window that is centered around the estimated decision boundary and uniformly draw sample from this window. Initially the window spans the entire feature space and is gradually constricted, where the rate of constriction models the exploration-exploitation tradeoff. The desired effect of this approach (CExp) is that we get an increasing sampling density in informative regions as active learning progresses, resulting in a continuous and natural transition from exploration to exploitation, limiting both premature exploitation and unnecessary exploration. We show that our approach outperforms state of the art on real world multiclass datasets. We also extend our approach to spatial mapping problems where the standard active learning assumption of uniform costs is violated. We show that we can take advantage of \emph{spatial continuity} in the data by geographically partitioning the instances in the sampling window and choosing a single partition (region) for sampling, as opposed to taking a random sample from the entire window, resulting in a novel spatial active learning algorithm that combines exploration and exploitation. We demonstrate that our approach (CExp-Spatial) can generate cost-effective sampling trajectories over baseline sampling methods. Finally, we present the real world problem of mapping benthic habitats where bathymetry derived features are typically not strong enough to discriminate the fine details between classes identified from high-resolution imagery, increasing the possiblity of model bias in active learning. We demonstrate, under such conditions, that CExp outperforms state of the art and that CExp-Spatial can generate more cost-effective sampling trajectories for an Autonomous Underwater Vehicle in contrast to baseline sampling strategies.
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Arens, Robert Segre Alberto Maria. "Learning to rank documents with support vector machines via active learning." [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/331.
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Nainabasti, Binod. "Role of Students’ Participation on Learning Physics in Active Learning Classes." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3022.
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Students’ interactions can be an influential component of students’ success in an interactive learning environment. From a participation perspective, learning is viewed in terms of how students transform their participation. However, many of the seminal papers discussing the participationist framework are vague on specific details about what student participation really looks like on a fine-grained scale. As part of a large project to understand the role of student participation in learning, this study gathered data that quantified students’ participation in three broad areas of two student-centered introductory calculus-based physics classes structured around the Investigative Science Learning Environment (ISLE) philosophy. These three broad areas of classes were in-class learning activities, class review sessions that happened at the beginning of every class, and the informal learning community that formed outside of class time.
Using video data, classroom observations, and students’ self-reported social network data, this study quantified students’ participation in these three aspects of the class throughout two semesters. The relationship between behaviors of students’ engagement in various settings of an active learning environment and (a) their conceptual understanding (measured by FCI gain) and (b) academic success in the courses as measured by exam scores and scores on out-of-class assignments were investigated. The results from the analysis of the student interaction in the learning process show that three class components, viz. the Review Session, Learning Activities, and Informal Learning Community, play distinct roles in learning. Students who come in the class with better content knowledge do not necessarily participate more in the learning activities of active learning classrooms. Learning Communities serve as a “support network” for students to finish assignments and help students to pass the course. Group discussions, which are facilitated by students themselves, better help students in gaining conceptual understanding. Since patterns of students’ participation do not change significantly over time, instructors should try to ensure greater participation by incorporating different learning activities in the active learning classroom.
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Arens, Robert James. "Learning to rank documents with support vector machines via active learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/331.
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Navigating through the debris of the information explosion requires powerful, flexible search tools. These tools must be both useful and useable; that is, they must do their jobs effectively without placing too many burdens on the user. While general interest search engines, such as Google, have addressed this latter challenge well, more topic-specific search engines, such as PubMed, have not. These search engines, though effective, often require training in their use, as well as in-depth knowledge of the domain over which they operate. Furthermore, search results are often returned in an order irrespective of users' preferences, forcing them to manually search through search results in order to find the documents they find most useful.
To solve these problems, we intend to learn ranking functions from user relevance preferences. Applying these ranking functions to search results allows us to improve search usability without having to reengineer existing, effective search engines. Using ranking SVMs and active learning techniques, we can effectively learn what is relevant to a user from relatively small amounts of preference data, and apply these learned models as ranking functions. This gives users the convenience of seeing relevance-ordered search results, which are tailored to their preferences as opposed to using a one-size-fits-all sorting method. As giving preference feedback does not require in-depth domain knowledge, this approach is suitable for use by domain experts as well as neophytes. Furthermore, giving preference feedback does not require a great deal of training, adding very little overhead to the search process.
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Chen, Si. "Active Learning Under Limited Interaction with Data Labeler." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104894.
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Active learning (AL) aims at reducing labeling effort by identifying the most valuable unlabeled data points from a large pool. Traditional AL frameworks have two limitations: First, they perform data selection in a multi-round manner, which is time-consuming and impractical. Second, they usually assume that there are a small amount of labeled data points available in the same domain as the data in the unlabeled pool.
In this thesis, we initiate the study of one-round active learning to solve the first issue. We propose DULO, a general framework for one-round setting based on the notion of data utility functions, which map a set of data points to some performance measure of the model trained on the set. We formulate the one-round active learning problem as data utility function maximization.
We then propose D²ULO on the basis of DULO as a solution that solves both issues. Specifically, D²ULO leverages the idea of domain adaptation (DA) to train a data utility model on source labeled data. The trained utility model can then be used to select high-utility data in the target domain and at the same time, provide an estimate for the utility of the selected data. Our experiments show that the proposed frameworks achieves better performance compared with state-of-the-art baselines in the same setting. Particularly, D²ULO is applicable to the scenario where the source and target labels have mismatches, which is not supported by the existing works.M.S.
Machine Learning (ML) has achieved huge success in recent years. Machine Learning technologies such as recommendation system, speech recognition and image recognition play an important role on human daily life. This success mainly build upon the use of large amount of labeled data: Compared with traditional programming, a ML algorithm does not rely on explicit instructions from human; instead, it takes the data along with the label as input, and aims to learn a function that can correctly map data to the label space by itself. However, data labeling requires human effort and could be time-consuming and expensive especially for datasets that contain domain-specific knowledge (e.g., disease prediction etc.) Active Learning (AL) is one of the solution to reduce data labeling effort. Specifically, the learning algorithm actively selects data points that provide more information for the model, hence a better model can be achieved with less labeled data. While traditional AL strategies do achieve good performance, it requires a small amount of labeled data as initialization and performs data selection in multi-round, which pose great challenge to its application, as there is no platform provide timely online interaction with data labeler and the interaction is often time inefficient. To deal with the limitations, we first propose DULO which a new setting of AL is studied: data selection is only allowed to be performed once. To further broaden the application of our method, we propose D²ULO which is built upon DULO and Domain Adaptation techniques to avoid the use of initial labeled data. Our experiments show that both of the proposed two frameworks achieve better performance compared with state-of-the-art baselines.
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Swiatocha, Andrea Leigh. "Learning through Movement." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/51847.
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Humans are designed to move. Movement is a key component of physical and mental maturation in children. It can take place in various settings, with different levels of intensity. During the developmental years of a child, it is imperative that a child is active. Most often movement and play are thought to occur outdoors. The idea of the"playground" activity does not have to be isolated to the outdoors. Children should be encouraged to be physically active in structured play, allowed free play with peers for social and emotional development, as well as learn through hands-on experiments that are important for their cognitive development. Play is how children experience their world and create new discoveries about themselves and others.
This thesis will be explored through the design of an elementary school for Alexandria, VA. An elementary school creates the perfect setting for which these elements of movement and learning to combine. This thesis explores the way in which the movement of the outdoor school yard can occur within the school building. The school grounds serve as demonstration to the community for active learning. Incorporating active design through elevation changes, material changes and the transition between indoor and outdoor allow the school to be a model for "learning through movement." This school also begins to address the larger issues of our society's unhealthy lifestyle by designing three levels of active design for the community, building, and individual child.Master of Architecture
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Bloodgood, Michael. "Active learning with support vector machines for imbalanced datasets and a method for stopping active learning based on stabilizing predictions." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 200 p, 2009. http://proquest.umi.com/pqdweb?did=1818417671&sid=1&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Calma, Adrian [Verfasser]. "Active learning with uncertain annotators : towards dedicated collaborative interactive learning / Adrian Calma." Kassel : kassel university press c/o Universität Kassel - Universitätsbibliothek, 2020. http://d-nb.info/1230907955/34.
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Huang, Jian Giles C. Lee. "A multiclass boosting classification method with active learning." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4765/index.html.
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Hasenjäger, Martina. "Active data selection in supervised and unsupervised learning." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=960209220.
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Jaulmes, Robin. "Active learning in partially observable Markov decision processes." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98733.
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People are efficient when they make decisions under uncertainty, even when their decisions have long-term ramifications, or when their knowledge and their perception of the environment are uncertain. We are able to experiment with the environment and learn, improving our behavior as experience is gathered. Most of the problems we face in real life are of that kind, and most of the problems that an automated agent would face in robotics too.Our goal is to build Artificial Intelligence algorithms able to reproduce the reasoning of humans for these complex problems. We use the Reinforcement Learning framework, which allows to learn optimal behaviors in dynamic environments. More precisely, we adapt Partially-Observable Markov Decision Processes (POMDPs) to environments that are partially known.
We take inspiration from the field of Active Learning: we assume the existence of an oracle, who can, during a short learning phase, provide the agent with additional information about its environment. The agent actively learns everything that is useful in the environment, with a minimum use of the oracle.
After reviewing existing methods for solving learning problems in partially observable environments, we expose a theoretical active learning setup. We propose an algorithm, MEDUSA, and show theoretical and empirical proofs of performance for it.