Dissertations / Theses on the topic 'Learning approach'
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Ouyang, Li. "Motivation, cultural values, learning processes, and learning in Chinese students." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1340.
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Bertsimas, Dimitris J., and Georgia Perakis. "Dynamic Pricing: A learning Approach." Massachusetts Institute of Technology, Operations Research Center, 2001. http://hdl.handle.net/1721.1/5314.
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We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, but rather assume parametric families of demand functions that are learned over time. We first consider the noncompetitive case and present dynamic programming algorithms of increasing computational intensity with incomplete state information for jointly estimating the demand and setting prices as time evolves. Our computational results suggest that dynamic programming based methods outperform myopic policies often significantly. We then extend our analysis in a competitive environment with two firms. We introduce a more sophisticated model of demand learning, in which the price elasticities are slowly varying functions of time, and allows for increased flexibility in the modeling of the demand. We propose methods based on optimization for jointly estimating the Firm's own demand, its competitor's demand, and setting prices. In preliminary computational work, we found that optimization based pricing methods offer increased expected revenue for a firm independently of the policy the competitor firm is following.
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Liston, Karina. "A mature approach to learning /." Title page, table of contents and abstract only, 1994. http://web4.library.adelaide.edu.au/theses/09ARPS/09arpsl773.pdf.
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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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Lai, Ling-yan Edith. "Effects of cooperative learning on student learning outcomes and approaches to learning in sixth form geography." Click to view the E-thesis via HKUTO, 1991. http://sunzi.lib.hku.hk/HKUTO/record/B38627292.
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Morri, Francesco. "A thermodynamic approach to deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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Neural Networks are an incredibly powerful tool used to solve complex problems.
The actual functioning of this tool and its behaviour when applied to different kind of problems is not completely explain though.
In this work we study the behaviour of a neural network, used to classify images, through a physical model, based on statistical thermodynamics. We found interesting results regarding the temperature of the different components of the network, that may be exploited in a more efficient training algorithm.
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Kong, Dan. "Learning-based approach for vision problems /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.
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CASTRO, THAIS HELENA CHAVES DE. "SYSTEMATIC APPROACH FOR GROUP PROGRAMMING LEARNING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18366@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOA investigação aqui relatada trata da concepção de elementos estruturantes para ampliar as oportunidades de intervenção pelo professor em um contexto de aprendizagem de programação em grupo. A partir de uma série de estudos de caso com turmas de calouros em cursos de computação, foi desenvolvida a sistematização de práticas, metodologias e tecnologias em uma abordagem para apoiar a aprendizagem de programação em grupo, baseada em três frentes de investigação: pressupostos pedagógicos, ferramentas LMS e métodos de colaboração. O eixo teórico referente à aprendizagem é a teoria de desenvolvimento cognitivo de Piaget, aliada a técnicas conhecidas de programação em grupo utilizadas no ensino de graduação em disciplinas introdutórias de programação. As ferramentas computacionais são utilizadas para monitorar e intervir durante o processo de aprendizagem. Nesse contexto, ambientes CSCL incentivam a colaboração e regulam as práticas desejadas. Nesta tese, outras tecnologias, como linguagens para representação de agentes e identificação de padrões são agregadas a eles para melhorar o acompanhamento e facilitar a intervenção. Por fim, como método de colaboração, é proposto um esquema progressivo de aprendizagem de programação em grupo, que auxilia os alunos a gradativamente adotarem práticas colaborativas na resolução de exercícios e que pode ser formalizado para incorporação a plataformas automatizadas.
The research reported here deals with devising structuring elements that may broaden intervention opportunities from the teacher in a context of group programming learning. Based on a set of case studies with freshmen in computing courses a systematization for practices, methods and technologies was developed producing an approach for supporting group programming based in three investigation paths: pedagogical assumptions, CSCL environments and collaboration methods. The main learning rationale is Jean Piaget’s Cognitive Development Theory, used alongside group programming techniques commonly applied in undergraduate introductory programming courses. Computational tools are used to monitor and intervene during learning process and in such context, CSCL environments encourage collaboration and regulate expected practices. In this thesis other technologies like languages for agent representation and patterning identification are also exploited for improving control and facilitate interventions. Finally, as collaboration method, it is proposed a Programming Progressive Learning Scheme that helps students to adopt collaborative practices when solving exercises and that can be formalized to be used with automated platforms.
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Chung, Ryan Kyong-doc. "Deep learning approach to metagenomic binning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119755.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-41).
Understanding the diversity and abundance of microbial populations is paramount to the health of humans and the environment. Estimating the diversity of these populations from whole metagenome shotgun (WMS) sequencing reads is difficult because the size of these datasets and overlapping reads limit what kinds of analysis we can do. Current methods require matching reads to a database of known microbes. These methods are either too slow or lack the sensitivity needed to identify novel species. We propose a convolutional neural network (CNN) based approach to metagenomic binning that embeds reads into a low-dimensional vector space based on taxonomic classification. We show that our method can get the speed and sensitivity necessary taxonomic classification. Our method was able to achieve 13% accuracy on identifying novel genus of bacteria as compared to 7% accuracy of k-mer embedding. At the same time, the speed of our method is within an order of magnitude of that of k-mer embedding, making it viable as a metagenomic analysis tool.
by Ryan Kyong-doc Chung.
M. Eng.
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Klivans, Adam R. "A complexity theoretic approach to learning." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8395.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.Includes bibliographical references (leaves 127-138).
This thesis details a new vantage point for attacking longstanding problems in machine learning. We use tools from computational complexity theory to make progress on problems from computational learning theory. Our methods yield the fastest and most expressive algorithms to date for learning several fundamental concept classes: * We show that any s-term DNF over n variables can be computed by a polynomial threshold function of order O(n1/3 log s). As an immediate consequence we obtain the fastest known DNF learning algorithm which runs in time 2O(n1/3). * We give the first polynomial time algorithm to learn an intersection of a constant number of halfspaces under the uniform distribution to within any constant error parameter. We also give the first quasipolynomial time algorithm for learning any function of a constant number of halfspaces with polynomial bounded weights under any distribution. * We give an algorithm to learn constant-depth polynomial-size circuits augmented with majority gates under the uniform distribution using random examples only. For circuits which contain a polylogarithmic number of majority gates the algorithm runs in quasipolynomial time. Under a suitable cryptographic assumption we show that these are the most expressive circuits which will admit a non-trivial learning algorithm. Our approach relies heavily on giving novel representations of well known concept classes via complexity theoretic reductions. We exploit the fact that many results in computational learning theory have a complexity theoretic analogue or implication. As such,
(cont.) we also obtain new results in computational complexity including (1) a proof that the 30 year old lower bound due to Minsky and Papert [88] on the degree of a perceptron computing a DNF formula is tight and (2) improved constructions of pseudo-random generators, mathematical objects which play a fundamental role in cryptography and derandomization.
by Adam Richard Klivans.
Ph.D.
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Thompson, Deborah R. "Life Science: An Outdoor Learning Approach." UNF Digital Commons, 1986. http://digitalcommons.unf.edu/etd/61.
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The instructional materials prepared for this project are based on outdoor activities that correlate with the Duval County, Florida, Performance Objectives for Life Science in the seventh grade. Special emphasis is placed on hands-on, sensory experiences and observations, and sequencing of instruction within lessons. The review of related literature includes the philosophy of outdoor/environmental education, a historical perspective of outdoor/environmental education, and learning theory as it applies to the principles and practices of an outdoor approach to education.
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Ruan, Yongshao. "Efficient inference : a machine learning approach /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/7009.
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Andersen, Linda, and Philip Andersson. "Deep Learning Approach for Diabetic Retinopathy Grading with Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279981.
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Diabetic retinopathy (DR) is a complication of diabetes and is a disease that affects the eyes. It is one of the leading causes of blindness in the Western world. As the number of people with diabetes grows globally, so does the number of people affected by diabetic retinopathy. This demand requires that better and more effective resources are developed in order to discover the disease in an early stage which is key to preventing that the disease progresses into more serious stages which ultimately could lead to blindness, and streamline further treatment of the disease. However, traditional manual screenings are not enough to meet this demand. This is where the role of computer-aided diagnosis comes in. The purpose of this report is to investigate how a convolutional neural network together with transfer learning can perform when trained for multiclass grading of diabetic retinopathy. In order to do this, a pre-built and pre-trained convolutional neural network from Keras was used and further trained and fine-tuned in Tensorflow on a 5-class DR grading dataset. Twenty training sessions were performed and accuracy, recall and specificity were evaluated in each session. The results show that testing accuracies achieved were in the range of 35% to 48.5%. The average testing recall achieved for class 0, 1, 2, 3 and 4 was 59.7%, 0.0%, 51.0%, 38.7% and 0.8%, respectively. Furthermore, the average testing specificity achieved for class 0, 1, 2, 3 and 4 was 77.8%, 100.0%, 62.4%, 80.2% and 99.7%, respectively. The average recall of 0.0% and average specificity of 100.0% for class 1 (mild DR) were obtained because the CNN model never predicted this class.Diabetisk näthinnesjukdom (DR) är en komplikation av diabetes och är en sjukdom som påverkar ögonen. Det är en av de största orsakerna till blindhet i västvärlden. Allt eftersom antalet människor med diabetes ökar, ökar även antalet med diabetisk näthinnesjukdom. Detta ställer högre krav på att bättre och effektivare resurser utvecklas för att kunna upptäcka sjukdomen i ett tidigt stadie, vilket är en förutsättning för att förhindra vidareutveckling av sjukdomen som i slutändan kan resultera i blindhet, och att vidare behandling av sjukdomen effektiviseras. Här spelar datorstödd diagnostik en viktig roll. Syftet med denna studie är att undersöka hur ett faltningsnätverk, tillsammans med överföringsinformation, kan prestera när det tränas för multiklass gradering av diabetisk näthinnesjukdom. För att göra detta användes ett färdigbyggt och färdigtränat faltningsnätverk, byggt i Keras, för att fortsättningsvis tränas och finjusteras i Tensorflow på ett 5-klassigt DR dataset. Totalt tjugo träningssessioner genomfördes och noggrannhet, sensitivitet och specificitet utvärderades i varje sådan session. Resultat visar att de uppnådda noggranheterna låg inom intervallet 35% till 48.5%. Den genomsnittliga testsensitiviteten för klass 0, 1, 2, 3 och 4 var 59.7%, 0.0%, 51.0%, 38.7% respektive 0.8%. Vidare uppnåddes en genomsnittlig testspecificitet för klass 1, 2, 3 och 4 på 77.8%, 100.0%, 62.4%, 80.2% respektive 99.7%. Den genomsnittliga sensitiviteten på 0.0% samt den genomsnittliga specificiteten på 100.0% för klass 1 (mild DR) erhölls eftersom CNN modellen aldrig förutsåg denna klass.
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Fang, Tongtong. "Learning from noisy labelsby importance reweighting: : a deep learning approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264125.
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Noisy labels could cause severe degradation to the classification performance. Especially for deep neural networks, noisy labels can be memorized and lead to poor generalization. Recently label noise robust deep learning has outperformed traditional shallow learning approaches in handling complex input data without prior knowledge of label noise generation. Learning from noisy labels by importance reweighting is well-studied. Existing work in this line using deep learning failed to provide reasonable importance reweighting criterion and thus got undesirable experimental performances. Targeting this knowledge gap and inspired by domain adaptation, we propose a novel label noise robust deep learning approach by importance reweighting. Noisy labeled training examples are weighted by minimizing the maximum mean discrepancy between the loss distributions of noisy labeled and clean labeled data. In experiments, the proposed approach outperforms other baselines. Results show a vast research potential of applying domain adaptation in label noise problem by bridging the two areas. Moreover, the proposed approach potentially motivate other interesting problems in domain adaptation by enabling importance reweighting to be used in deep learning.Felaktiga annoteringar kan sänka klassificeringsprestanda.Speciellt för djupa nätverk kan detta leda till dålig generalisering. Nyligen har brusrobust djup inlärning överträffat andra inlärningsmetoder när det gäller hantering av komplexa indata Befintligta resultat från djup inlärning kan dock inte tillhandahålla rimliga viktomfördelningskriterier. För att hantera detta kunskapsgap och inspirerat av domänanpassning föreslår vi en ny robust djup inlärningsmetod som använder omviktning. Omviktningen görs genom att minimera den maximala medelavvikelsen mellan förlustfördelningen av felmärkta och korrekt märkta data. I experiment slår den föreslagna metoden andra metoder. Resultaten visar en stor forskningspotential för att tillämpa domänanpassning. Dessutom motiverar den föreslagna metoden undersökningar av andra intressanta problem inom domänanpassning genom att möjliggöra smarta omviktningar.
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Headrick, Jonathon Jeffs. "Affective learning design: A principled approach to emotion in learning." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/89755/1/Jonathon_Headrick_Thesis.pdf.
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This PhD project set out to explore the role of emotion during learning in sport, focusing on how actions, emotions and cognitions interact under the influence of constraints. Key outcomes include the development of the theoretical concept - Affective Learning Design, and a new tool for assessing the intensity of emotions during learning - the Sport Learning and Emotions Questionnaire. The findings presented in this thesis provide both theoretical and practical implications discussing why emotion should be considered in the design of learning environments in sport.
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Dalton-Brits, E., and M. Viljoen. "Personality traits and learning approaches : are they influencing the learning process?" Journal for New Generation Sciences, Vol 8, Issue 3: Central University of Technology, Free State, Bloemfontein, 2010. http://hdl.handle.net/11462/565.
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Published ArticleThe relationship between the big five personality traits, Extraversion, Agreeableness Neuroticism, Conscientiousness and Openness to Experience and deep and surface approaches to learning forms the basis of this article. The findings of a research study in this milieu will be presented to prove that earlier studies in this field have been upheld, but that an important deviation has occurred on certain levels of personality. A students way of learning implies the type of learning that is taking place. Ultimately we as lecturers want to encourage deep learning as this stimulates retention of information, important in production of students that are ready for employment.
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Choy, Sarojni C. "Youth learning." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/36660/1/36660_Digitised%20Thesis.pdf.
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There is an abundance of literature on research about teaching and learning in the tertiary education sector. Within this body of literature there lies the field of andragogy that focuses on the facilitation of adult learning. Although adults and youth (those aged 17-24 years) often share common learning environments, where several principles of andragogy are practiced, there is no evidence of research to ascertain whether such principles apply to youth learners.
The primary purpose of this thesis was to examine whether youth learned like adults. Three characteristics that most adult learners share are: a deep approach to learning, an andragogical orientation to study and a high level of readiness for self-directed learning. The thesis firstly investigated whether youth learners also shared these characteristics and then explored the factors that contributed to their learning.
Altogether, 450 youth who were enrolled in courses offered by universities and Technical and Further Education (TAFE) fustitutes completed three survey questionnaires. The Study Process Questionnaire gathered data about their learning approaches, the Student Orientation Questionnaire collected data about their study orientation and the Learning Preference Assessment questionnaire informed about their level of readiness for self-directed learning. The quantitative data from the survey were analysed using the SPSS computer software. Two analytical models were developed to ascertain whether youth learned like adults.
The findings from both analytical models concluded that most youth did not learn like adult learners. Unlike adults, most youth learners had a surface approach to learning, a preference for pedagogical as well as andragogical orientations to study and low levels of readiness for self-directed learning. The survey results showed that youth preferred only the 'feel good' aspects of andragogy.
Focus groups were arranged with volunteer youths who had participated in the survey. A proforma was used to explore youths' perspectives of the factors that contributed to their learning as illustrated by the survey results. Youths' teachers were interviewed to gather their perspectives of factors that contributed to youths' learning.
A number of factors were identified during the focus groups and interviews. An analytical framework was developed to examine the factors. Two major themes emerged from the data: lifeworld and formal learning environment. Factors within each of these appeared to influence youths' decision making about how they chose to learn. Factors within youths' lifeworld related to their role conflict, expectancy valence and personological attributes. Institutional systems, teachers and their practices, decision making in system-related matters, and opportunities for self-directed learning, critical thinking and reflective thinking were perceived to be the main factors within the formal learning environment that contributed to youth's learning.
The findings from the survey and focus group data were used to draw a profile of youth learners in terms of their priorities, motivation and learning attributes. The findings were also used for discussions relating to the six principles of adult learning. A set of principles for practice and skilling for higher learning were suggested for youth learners. Based on the results of this exploratory study, the following conclusion about youth learning was proposed:
Most youth use a surface approach to learning, are at Stage 2 of their learning on an orthogonal scale and have low level of readiness for self-directed learning.
Most youth seem to appreciate a relational level of understanding rather than abstract thinking. Youths' learning could be better facilitated using Kolb's learning theory such that their learning could begin with concrete experience followed by reflective observation and then abstract conceptualisation. A directive, but highly supportive approach where the facilitator plays the role of a motivator and guide, is recommended for youth learners.
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Roobaert, Danny. "Pedagogical support vector learning : a pure learning approach to object recognition." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3166.
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Moxness, Katherine. "The effects of concept mapping on learning approach and meaningful learning /." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=60560.
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Two hundred and nine undergraduate students enrolled in an introductory Anthropology course were pre-tested using the Learning and Study Strategy Inventories (LASSI) to establish their learning approach. Concept mapping was used to alter a student's learning approach from a non-creative to a creative approach. Students were then post-tested using the LASSI to evaluate the learning intervention. The first hypothesis proposed that non-creative learners would become more holistic and creative learners as a result of the concept mapping intervention. No significant treatment effects were found. Non-creative learners made significant gains in concentration from pre to post testing. It was also hypothesized that certain demographic variables would help explain the learning approach a student demonstrated. Science students had the highest mean attitude, motivation, concentration and time management and use of test strategies. Anthropology students had the highest anxiety, and arts students increased on information processing. Nineteen year olds were the most motivated and attitude decreased with age. Second year students who had taken a previous course in anthropology had higher mean attitudes, motivation, concentration, selecting main ideas, and use of test strategies when compared to second year students who hadn't taken a previous course. Science students increased their mean use of test strategies regardless of previous course work. Overall, the mean use of test strategies increased regardless of faculty affiliation had a student taken a previous course.
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Fok, Po-yan, and 霍寶欣. "Can a constructivist learning environment enhance a deep approach to learning?" Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31962956.
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Fok, Po-yan. "Can a constructivist learning environment enhance a deep approach to learning?" Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B26232789.
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Foreman, Samuel Alfred. "Learning better physics: a machine learning approach to lattice gauge theory." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6944.
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In this work we explore how lattice gauge theory stands to benefit from new developments in machine learning, and look at two specific examples that illustrate this point. We begin with a brief overview of selected topics in machine learning for those who may be unfamiliar, and provide a simple example that helps to show how these ideas are carried out in practice.
After providing the relevant background information, we then introduce an example of renormalization group (RG) transformations, inspired by the tensor RG, that can be used for arbitrary image sets, and look at applying this idea to equilibrium configurations of the two-dimensional Ising model.
The second main idea presented in this thesis involves using machine learning to improve the efficiency of Markov Chain Monte Carlo (MCMC) methods. Explicitly, we describe a new technique for performing Hamiltonian Monte Carlo (HMC) simulations using an alternative leapfrog integrator that is parameterized by weights in a neural network. This work is based on the L2HMC ('Learning to Hamiltonian Monte Carlo') algorithm introduced in [1].
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Yamanashi, Julie E. "Children helping children : a cooperative learning approach /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17809.pdf.
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Ewö, Christian. "A machine learning approach in financial markets." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5571.
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In this work we compare the prediction performance of three optimized technical indicators with a Support Vector Machine Neural Network. For the indicator part we picked the common used indicators: Relative Strength Index, Moving Average Convergence Divergence and Stochastic Oscillator. For the Support Vector Machine we used a radial-basis kernel function and regression mode. The techniques were applied on financial time series brought from the Swedish stock market. The comparison and the promising results should be of interest for both finance people using the techniques in practice, as well as software companies and similar considering to implement the techniques in their products.
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Svensson, Oskar, and Simon Thelin. "Indirect Tire Monitoring System - Machine Learning Approach." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34290.
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The heavy duty vehicle industry has today no requirement to providea tire pressure monitoring system by law. This has created issues sur-rounding unknown tire pressure and thread depth during active service.There is also no standardization for these kind of systems which meansthat different manufacturers and third party solutions work after theirown principles and it can be hard to know what works for a given vehicletype. National Highway Traffic Safety Administration (NHTSA) put out a new study that determined that underinflated tires of 25% or less are 3 times more likely to be involved in a crash related to tire issues versus vehicles with properly inflated tires. The objective for this thesis is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. Drivec Bridge hardware interprets existing sensors from the vehicle. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues.The classifier will classify the vehicles tires condition. The classifier will be implemented in Drivecs cloud service and use data to classify the tires condition. The resulting classifier of the project is a random forest implemented in Python. The result from the front axle with a dataset consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of90.54% (±0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. The project group has verified with high accuracy that a vehicle has been classified as bad and then been reclassified as good over a time span of 16 days. At the first day offboard measurements were performed and it showed that the tires of the front axle were underinflated. The classifier indicated that the vehicle had bad classifications until day 14. At this day an offboard measurement was performed and it was concluded that they were no longer underinflated and the classifier indicated this as well. To verify the result the workshop was contacted and verified that the vehicle had changed tires of the front axle at day 14. This has verified that the classifier is able to detect change and stay consistent in the results over a longer time period.
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Zhang, Jian. "The LEP Learning System, an IVSA approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0008/NQ39168.pdf.
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Desjardins, Charles. "Cooperative Adaptive Cruise Control: A Learning Approach." Thesis, Université Laval, 2009. http://www.theses.ulaval.ca/2009/26048/26048.pdf.
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L'augmentation dans les dernières décennies du nombre de véhicules présents sur les
routes ne s'est pas passée sans son lot d'impacts négatifs sur la société. Même s'ils ont
joué un rôle important dans le développement économique des régions urbaines à travers
le monde, les véhicules sont aussi responsables d'impacts négatifs sur les entreprises, car
l'inefficacité du
ot de traffic cause chaque jour d'importantes pertes en productivité.
De plus, la sécurité des passagers est toujours problématique car les accidents de voiture
sont encore aujourd'hui parmi les premières causes de blessures et de morts accidentelles
dans les pays industrialisés. Ces dernières années, les aspects environnementaux ont
aussi pris de plus en plus de place dans l'esprit des consommateurs, qui demandent
désormais des véhicules efficaces au niveau énergétique et minimisant leurs impacts sur
l'environnement.
évidemment, les gouvernements de pays industrialisés ainsi que les manufacturiers
de véhicules sont conscients de ces problèmes et tentent de développer des technologies
capables de les résoudre. Parmi les travaux de recherche en ce sens, le domaine des
Systèmes de Transport Intelligents (STI) a récemment reçu beaucoup d'attention. Ces
systèmes proposent d'intégrer des systèmes électroniques avancés dans le développement
de solutions intelligentes conçues pour résoudre les problèmes liés au transport automobile
cités plus haut.
Ce mémoire se penche donc sur un sous-domaine des STI qui étudie la résolution de
ces problèmes gr^ace au développement de véhicules intelligents. Plus particulièrement,
ce mémoire propose d'utiliser une approche relativement nouvelle de conception de tels
systèmes, basée sur l'apprentissage machine. Ce mémoire va donc montrer comment
les techniques d'apprentissage par renforcement peuvent être utilisées afin d'obtenir
des contrôleurs capables d'effectuer le suivi automatisés de véhicules. Même si ces
efforts de développement en sont encore à une étape préliminaire, ce mémoire illustre
bien le potentiel de telles approches pour le développement futur de véhicules plus
\intelligents".The impressive growth, in the past decades, of the number of vehicles on the road has not come without its share of negative impacts on society. Even though vehicles play an active role in the economical development of urban regions around the world, they unfortunately also have negative effects on businesses as the poor efficiency of the traffic ow results in important losses in productivity each day. Moreover, numerous concerns have been raised in relation to the safety of passengers, as automotive transportation is still among the first causes of accidental casualties in developed countries. In recent years, environmental issues have also been taking more and more place in the mind of customers, that now demand energy-efficient vehicles that limit the impacts on the environment. Of course, both the governments of industrialized countries and the vehicle manufacturers have been aware of these problems, and have been trying to develop technologies in order to solve these issues. Among these research efforts, the field of Intelligent Transportation Systems (ITS) has been gathering much interest as of late, as it is considered an efficient approach to tackle these problems. ITS propose to integrate advanced electronic systems in the development of intelligent solutions designed to address the current issues of automotive transportation. This thesis focuses on a sub-field ITS since it studies the resolution of these problems through the development of Intelligent Vehicle (IV) systems. In particular, this thesis proposes a relatively novel approach for the design of such systems, based on modern machine learning. More specifically, it shows how reinforcement learning techniques can be used in order to obtain an autonomous vehicle controller for longitudinal vehiclefollowing behavior. Even if these efforts are still at a preliminary stage, this thesis illustrates the potential of using these approaches for future development of \intelligent" vehicles.
Inscrit au Tableau d'honneur de la Faculté des études supérieures
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SpasicÌ, Irena. "A machine learning approach to term classification." Thesis, University of Salford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419298.
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Ainslie, Susan. "Intensive language learning : A multi-media approach." Thesis, University of Brighton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309004.
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Alsallal, M. "A machine learning approach for plagiarism detection." Thesis, Coventry University, 2016. http://curve.coventry.ac.uk/open/items/7e903a56-4845-4852-b1a8-2849b1cdb08a/1.
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Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases. Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author. The Intrinsic method aims to generate a model of author “style” by revealing a set of certain features of authorship. The model’s generation procedure focuses on just one author as an attempt to summarise aspects of an author’s style in a definitive and clear-cut manner. The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists.
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Kostopouls, Theodore P. "A Machine Learning approach to Febrile Classification." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1173.
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General health screening is needed to decrease the risk of pandemic in high volume areas. Thermal characterization, via infrared imaging, is an effective technique for fever detection, however, strict use requirements in combination with highly controlled environmental conditions compromise the practicality of such a system. Combining advanced processing techniques to thermograms of individuals can remove some of these requirements allowing for more flexible classification algorithms. The purpose of this research was to identify individuals who had febrile status utilizing modern thermal imaging and machine learning techniques in a minimally controlled setting. Two methods were evaluated with data that contained environmental, and acclimation noise due to data gathering technique. The first was a pretrained VGG16 Convolutional Neural Network found to have F1 score of 0.77 (accuracy of 76%) on a balanced dataset. The second was a VGG16 Feature Extractor that gives inputs to a principle components analysis and utilizes a support vector machine for classification. This technique obtained a F1 score of 0.84 (accuracy of 85%) on balanced data sets. These results demonstrate that machine learning is an extremely viable technique to classify febrile status independent of noise affiliated.
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Steinbach, Carl W. (Carl William) 1980. "A reinforcement-learning approach to power management." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87834.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 51-53).
by Carl W. Steinbach.
M.Eng.
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Sheth, Beerud Dilip. "A learning approach to personalized information filtering." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/37998.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 96-100).
by Beerud Dilip Sheth.
M.S.
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Allard, Nathan, and Tobias Hagström. "Modern Housing Valuation : A Machine Learning Approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301866.
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The primary goal of this report was to examine and demonstrate how machine learning methods can be used to produce accurate and useful apartment valuation models, focusing on the Stockholm market. Furthermore, the paper analyzed how different attributes, including apartment descriptions, affect the price of an apartment. Accurate and efficient valuation could not only be useful for individual property owners, who want a quick and precise valuation, but also for real estate agencies and tax authorities. To this end, several different methods were applied and evaluated, the best of which achieved a MAPE of 6.37%. Some of the most important features in relation to apartment price included: rent, construction year and a wide range of location related variables. It was concluded that machine learning methods can produce accurate and useful real estate valuation models, outperforming real estate agent’s manual appraisals. In addition, location based features were identified as the most important whilst bathroom and kitchen condition was not as important as expected. Furthermore, whilst the models developed in this report did not manage to utilize ”agent written” real estate descriptions, the results indicate that there is valuable information to be extracted, provided a more rigorous pre- processing and analysis of the data is conducted.Det huvudsakliga målet med denna rapport var att undersöka och demonstrera hur maskininlärningsmetoder kan användas för att skapa exakta och användbara bostadsvärderingsmodeller, med fokus på Stockholmsmarknaden. Vidare analyseras även om och hur olika attribut, inklusive lägenhetsbeskrivningar, påverkar priset på lägenheten. Exakt och effektiv värdering skulle inte bara vara användbart för privatpersoner, som vill ha en snabb och precis värdering, men också för mäklare och skattemyndigheter. För detta ändamål applicerades och utvärderades ett flertal metoder, varav den bästa uppnådde en MAPE på 6.37%. Avgift, byggnadsår och ett flertal olika geografiskt relaterade variabler var bland de viktigaste vad gäller lägenhetspriset. För det första drogs slutsatsen att maskininläarningsmetoder kan producera exakta och användbara bostadsvärderingsmodeller, med högre precision än mäklares värderingar. För det andra identifierades geografiskt baserade attribut som mest väsentliga, medan skicket på badrum och kök inte var mindre viktigt än förväntat. Avslutningsvis kan konstateras att även om modellerna som utvecklades i rapporten inte lyckades utnyttja lägenhetsbeskrivningarna, indikerar resultatet att de innehåller värdefull information som potentiellt kan utnyttjas, givet att en mer rigorös förbehandling och analys av datan utförs.
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Rao, Anantha N. "Learning-based Visual Odometry - A Transformer Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658636420617.
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Parascandolo, Fiorenzo. "Trading System: a Deep Reinforcement Learning Approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Find full textAbstract:
The main objective of this work is to show the advantages of Reinforcement Learning-based approaches to develop a Trading System. The experimental results showed the great adaptability of the developed models, which obtained very satisfactory econometric performances in five datasets of Forex Market characterized by different volatilities. The TradingEnv baseline provided by OpenAi was used to simulate the financial market.
The latter has been improved by implementing a rendering of the simulation and the commission plan applied by a real Electronic Communication Network. As regards the artificial agent, the main contributions are the use of the Gramian Angular Field transformation to encode the historical financial series in images
and the experimental proof that the presence of Locally Connected Layers brings a benefit in terms of performances. Vanilla Saliency Map was used as an explainability method to tune the window size of the observations of the environment.
From the explanation of the best performing model it is possible to observe how the most important information are the price changes observed with greater granularity in accordance with the theoretical results proven at the state of the art on the historical financial series.
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VANCE, DANNY W. "AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1162335608.
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Yaokai, Yang. "Effective Phishing Detection Using Machine Learning Approach." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1544189633297122.
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Falgén, Enqvist Olle. "Cardinality estimation with a machine learning approach." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288909.
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This thesis investigates how three different machine learning models perform on cardinalty estimation for sql queries. All three models were evaluated on three different data sets. The models were tested on both estimating cardinalities when the query just takes information from one table and also a two way join case. Postgresql's own cardinality estimator was used as a baseline. The evaluated models were: Artificial neural networks, random forests and extreme gradient boosted trees. What was found is that the model that performs best is the extreme gradient boosted tree with a tweedie regression loss function. To the authors knowledge, this is the first time an extreme gradient boosted tree has been used in this context.Denna uppsats undersöker hur tre olika maskininlärningsmodeller presterar på kardinalitetsuppskattning för sql förfrågningar till en databas. Alla tre modeller utvärderades på tre olika datauppsättningar. Modellerna fick både behandla förfrågningar från en tabell, samt en sammanslagning mellan två tabeller. Postgresql's egna kardinalitetsestimerare användes som referenspunkt. De utvärderade modellerna var följande: artificiella neurala nätverk, random forests och extreme gradient boosted trees. En slutsats var att den modellen som utförde uppgiften bäst var extreme gradient boosted trees med en tweedie-regression förlustfunktion. Såvitt författaren vet är det här första gången den här typen av extreme gradient boosted tree används på denna typ av problem.
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Alazemi, Ahmad HMH. "Deep Learning Approach to Structure From Polarization." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1627820674064972.
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Lau, Tessa. "Programming by demonstration : a machine learning approach /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6949.
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Anderson, Corin R. "A machine learning approach to Web personalization /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6875.
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Wang, Gang. "Solution path algorithms : an efficient model selection approach /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANGG.
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Murray-Smith, Roderick. "A local model network approach to nonlinear modelling." Thesis, University of Strathclyde, 1994. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27067.
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This thesis describes practical learning systems able to model unknown nonlinear dynamic processes from their observed input-output behaviour. Local Model Networks use a number of simple, locally accurate models to represent a globally complex process, and provide a powerful, flexible framework for the integration of different model structures and learning algorithms. A major difficulty with Local Model Nets is the optimisation of the model structure. A novel Multi-Resolution Constructive (MRC) structure identification algorithm for local model networks is developed. The algorithm gradually adds to the model structure by searching for 'complexity' at ever decreasing scales of 'locality'. Reliable error estimates are useful during development and use of models. New methods are described which use the local basis function structure to provide interpolated state-dependent estimates of model accuracy. Active learning methods which automatically construct a training set for a given Local Model structure are developed, letting the training set grow in step with the model structure - the learning system 'explores' its data set looking for useful information. Local Learning methods developed in this work are explicitly linked to the local nature of the basis functions and provide a more computationally efficient method, more interpretable models and, due to the poor conditioning of the parameter estimation problem, often lead to an improvement in generalisation, compared to global optimisation methods. Important side-effects of normalisation of the basis functions are examined. A new hierarchical extension of Local Model Nets is presented: the Learning Hierarchy of Models (LHM), where local models can be sub-networks, leading to a tree-like hierarchy of softly interpolated local models. Constructive model structure identification algorithms are described, and the advantages of hierarchical 'divide-and-conquer' methods for modelling, especially in high dimensional spaces are discussed. The structures and algorithms are illustrated using several synthetic examples of nonlinear multivariable systems (dynamic and static), and applied to real world examples. Two nonlinear dynamic applications are described: predicting the strip thickness in an aluminium rolling mill from observed process data, and modelling robot actuator nonlinearities from measured data. The Local Model Nets reliably constructed models which provided the best results to date on the Rolling Mill application.
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Stanzione, Vincenzo Maria. "Developing a new approach for machine learning explainability combining local and global model-agnostic approaches." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25480/.
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The last couple of past decades have seen a new flourishing season for the Artificial
Intelligence, in particular for Machine Learning (ML). This
is reflected in the great number of fields that are employing ML solutions to overcome a broad spectrum of problems.
However, most of the last employed ML models have a black-box
behavior. This means that given a certain input, we are not able to understand
why one of these models produced a certain output or made a certain
decision. Most of the time, we are not interested in knowing what
and how the model is thinking, but if we think of a model which makes extremely critical
decisions or takes
decisions that have a heavy result on people’s lives, in these cases explainability is a duty.
A great variety of techniques to perform global or local explanations are available. One of the most widespread is Local Interpretable Model-Agnostic
Explanations (LIME), which creates a local linear model in the proximity of an
input to understand in which way each feature contributes to the final output.
However, LIME is not immune from instability problems and sometimes to
incoherent predictions. Furthermore, as a local explainability technique, LIME
needs to be performed for each different input that we want to explain.
In this work, we have been inspired by the LIME approach for linear models
to craft a novel technique. In combination with the Model-based Recursive Partitioning
(MOB), a brand-new score function to assess the quality of a partition
and the usage of Sobol quasi-Montecarlo sampling, we developed a new global
model-agnostic explainability technique we called Global-Lime.
Global-Lime
is capable of giving a global understanding of the original ML
model, through an ensemble of spatially not overlapped hyperplanes, plus a local
explanation for a certain output considering only the corresponding linear
approximation. The idea is to train the black-box model and then supply along
with it its explainable version.
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Van, Rheede Van Oudtshoorn Lynette Moira. "Conversational structures in organisation learning: A self-organised learning approach to counselling." Thesis, Brunel University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488673.
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This study is concerned with the development of a conversational system for promoting the emotional health of employees in organisations. It is also concerned with improving the health of the organisation as a whole, by enabling the organisation to learn from the experience of its individual members.
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Xue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.
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Le but de cette thèse est de minimiser la perte de performance d'un système de détection lorsqu'il rencontre un changement de distribution de données à la suite d’un événement connu (maintenance, ajout de capteur etc.). L'idée est d'utiliser l'approche d'apprentissage par transfert pour exploiter l'information apprise avant l’événement pour adapter le détecteur au système modifié. Un modèle d'apprentissage multitâche est proposé pour résoudre ce problème. Il utilise un paramètre pour équilibrer la quantité d'informations apportées par l'ancien système par rapport au nouveau. Ce modèle est formalisé de manière à pouvoir être résolu par un SVM mono-classe classique avec une matrice de noyau spécifique. Pour sélectionner le paramètre de contrôle, une méthode qui calcule les solutions pour toutes les valeurs du paramètre introduit et un critère de sélection de sa valeur optimale sont proposés. Les expériences menées dans le cas de changement de distribution et d’ajout de capteurs montrent que ce modèle permet une transition en douceur de l'ancien système vers le nouveau. De plus, comme le modèle proposé peut être formulé comme un SVM mono-classe classique, des algorithmes d'apprentissage en ligne pour SVM mono-classe sont étudiés dans le but d'obtenir un taux de fausses alarmes stable au cours de la phase de transition. Ils peuvent être appliqués directement à l'apprentissage en ligne du modèle proposéThe aim of this thesis is to minimize the performance loss of a one-class detection system when it encounters a data distribution change. The idea is to use transfer learning approach to transfer learned information from related old task to the new one. According to the practical applications, we divide this transfer learning problem into two parts, one part is the transfer learning in homogenous space and the other part is in heterogeneous space. A multi-task learning model is proposed to solve the above problem; it uses one parameter to balance the amount of information brought by the old task versus the new task. This model is formalized so that it can be solved by classical one-class SVM except with a different kernel matrix. To select the control parameter, a kernel path solution method is proposed. It computes all the solutions along that introduced parameter and criteria are proposed to choose the corresponding optimal solution at given number of new samples. Experiments show that this model can give a smooth transition from the old detection system to the new one whenever it encounters a data distribution change. Moreover, as the proposed model can be solved by classical one-class SVM, online learning algorithms for one-class SVM are studied later in the purpose of getting a constant false alarm rate. It can be applied to the online learning of the proposed model directly
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Yang, Zhaoyuan Yang. "Adversarial Reinforcement Learning for Control System Design: A Deep Reinforcement Learning Approach." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu152411491981452.
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Papadopoulos-Korfiatis, Alexandros. "Autopoietic approach to cultural transmission." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28773.
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Non-representational cognitive science is a promising research field that provides an alternative to the view of the brain as a “computer” filled with symbolic representations of the world and cognition as “calculations” performed on those symbols. Autopoiesis is a biological, bottom-up, non-representational theory of cognition, in which representations and meaning are framed as explanatory concepts that are constituted in an observer’s description of a cognitive system, not operational concepts in the system itself. One of the problems of autopoiesis, and all non-representational theories, is that they struggle with scaling up to high-level cognitive behaviour such as language. The Iterated Learning Model is a theory of language evolution that shows that certain features of language are explained not because of something happening in the linguistic agent’s brain, but as the product of the evolution of the linguistic system itself under the pressures of learnability and expressivity. Our goal in this work is to combine an autopoietic approach with the cultural transmission chains that the ILM uses, in order to provide the first step in an autopoietic explanation of the evolution of language. In order to do that, we introduce a simple, joint action physical task in which agents are rewarded for dancing around each other in either of two directions, left or right. The agents are simulated e-pucks, with continuous-time recurrent neural networks as nervous systems. First, we adapt a biologically plausible reinforcement learning algorithm based on spike-timing dependent plasticity tagging and dopamine reward signals. We show that, using this algorithm, our agents can successfully learn the left/right dancing task and examine how learning time influences the agents’ task success rates. Following that, we link individual learning episodes in cultural transmission chains and show that an expert agent’s initial behaviour is successfully transmitted in long chains. We investigate the conditions under which these transmission chains break down, as well as the emergence of behaviour in the absence of expert agents. By using long transmission chains, we look at the boundary conditions for the re-establishment of transmitted behaviour after chain breakdowns. Bringing all the above experiments together, we discuss their significance for non-representational cognitive science and draw some interesting parallels to existing Iterated Learning research; finally, we close by putting forward a number of ideas for additions and future research directions.
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Nikolic, Marko. "Single asset trading: a recurrent reinforcement learning approach." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-47505.
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Asset trading using machine learning has become popular within the financial industry in the recent years. This can for instance be seen in the large number of daily trading volume which are defined by an automatic algorithm. This thesis presents a recurrent reinforcement learning model to trade an asset. The benefits, drawdowns and the derivations of the model are presented. Different parameters of the model are calibrated and tuned considering a traditional division between training and testing data set and also with the help of nested cross validation. The results of the single asset trading model are compared to the benchmark strategy, which consists of buying the underlying asset and hold it for a long period of time regardless of the asset volatility. The proposed model outperforms the buy and hold strategy on three out of four stocks selected for the experiment. Additionally, returns of the model are sensitive to changes in epoch, m, learning rate and training/test ratio.