Relevant bibliographies by topics / Modeling Learning / Dissertations / Theses
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Dissertations / Theses on the topic 'Modeling Learning'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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1

SILVA, DIVA DE SOUZA E. "MODELING LEARNING OBJECTS COMPOSITION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8668@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O desenvolvimento de conteúdos instrucionais utilizando as novas tecnologias de informação é um processo caro, demorado e complexo, que aponta para o estabelecimento de novas metodologias. É neste contexto que surge o conceito de Objeto de Aprendizagem (LO), cujo enfoque está em promover a reutilização do conteúdo. Entretanto, ao considerar o reuso de conteúdo, também se observa uma necessidade de seqüência - lo para formar conteúdos mais elaborados ou mais complexos. Nesta tese adota-se uma estratégia de representar LOs cada vez men
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2

Avellino, Paul D. "Learning adversary modeling from games." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Sep%5FAvellino.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2007.<br>Thesis Advisor(s): Martell, Craig H. "September 2007." Description based on title screen as viewed on October 22, 2007. Includes bibliographical references (p. 77-79). Also available in print.
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3

Robbins, Anna, and Gustav Vittberg. "Machine learning for constitutive modeling." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299787.

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Constitutive modeling of nonlinear materials is a computationally complex and time-intensive process. To mitigate this, a neural network was used to simulate complex behaviors of non-linear materials. With supervised learning the machine learning model was able to predict the stresses in a material when given the strains. The machine learning algorithm predicted stresses in a linear elastic material with high accuracy, and in a hyperelastic material with lower accuracy. To simulate experimental conditions, artificial Gaussian noise was added to the strain data, and the model was tested with th
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4

Carroll, Glenn R. "Learning probabilistic grammars for language modeling." [S.l.] : Universität Stuttgart , Fakultätsübergreifend / Sonstige Einrichtung, 1995. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB7084251.

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5

Liu, Hairong. "Student modeling in e-learning environments /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1420936.

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6

Menier, Emmanuel. "Deep Learning for Reduced Order Modeling." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG004.

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Les systèmes dynamiques sont généralement modélisés à l'aide d'équations aux dérivées partielles (EDP). Ces modèles sont étroitement liés à la façon dont les scientifiques observent le monde et, en tant que tels, ils sont limités par notre compréhension des systèmes étudiés. En effet, des modèles tels que les équations de Navier-Stokes ne modélisent que les interactions locales dans un écoulement, et négligent les phénomènes sous-jacents qui contrôlent le système dans son ensemble. Cela conduit souvent à des coûts de calcul excessifs associés à la résolution numérique des EDP. Dans cette thèse
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7

Goutierre, Emmanuel. "Machine learning-based particle accelerator modeling." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG106.

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Les accélérateurs de particules reposent sur des simulations de haute précision pour optimiser la dynamique du faisceau. Ces simulations sont coûteuses en ressources de calcul, rendant leur analyse en temps réel difficilement réalisable. Cette thèse propose de surmonter cette limitation en explorant le potentiel de l'apprentissage automatique pour développer des modèles de substitution des simulations d'accélérateurs de particules. Ce travail se concentre sur ThomX, une source Compton compacte, et introduit deux modèles de substitution : LinacNet et Implicit Neural ODE (INODE). Ces modèles son
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8

Sasal, Léna. "Deep Learning for Modeling Dynamical System." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS001.

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L'apprentissage profond a montré un potentiel remarquable dans divers domaines, en particulier dans la modélisation et la prévision de phénomènes temporels et spatio-temporels complexes. Cependant, des défis subsistent dans la capture précise des relations non linéaires et dans la gestion de données structurées en graphe. Cette thèse vise à relever ces défis à travers plusieurs axes clés : l'amélioration des prévisions de séries temporelles en utilisant des modèles d'apprentissage profond hybrides, l'avancement des méthodes basées sur les graphes pour analyser les dynamiques temporelles et spa
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9

Deniz, Juan C. (Deniz Carlos) 1976. "Learning theory applications to product design modeling." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89269.

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10

Enver, Asad. "Modeling Trouble Ticket ResolutionTime Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176779.

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This thesis work, conducted at Telenor Sweden, aims to build a model that would try to accurately predict the resolution time of Priority 4 Trouble Tickets. (Priority 4 trouble tickets are those tickets that get generated more often-e in higher volumes per month). It explores and investigates the possibility of applying Machine Learning and Deep Learning techniques to trouble ticket data to find an optimal solution that performs better than the current method in place (which is explained in Section 3.5). The model would be used by Telenor to inform the end-users of when the networks team expec
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11

Elabid, Zakaria. "Informed deep learning for modeling physical dynamics." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS006.

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La modélisation des systèmes statiques et dynamiques a été révolutionnée par l'apprentissage profond, qui a offert des outils puissants pour la prévision et l'adaptation à des phénomènes physiques complexes. Cependant, les réseaux de neurones traditionnels peinent souvent à se généraliser, en particulier dans des scénarios hors distribution et avec des ensembles de données réduits, ce qui limite leur application dans des contextes réels. Cette thèse aborde ces défis en proposant des méthodes novatrices qui intègrent directement les lois physiques dans les cadres d'apprentissage profond. La pre
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12

Bowring, James Frederick. "Modeling and Predicting Software Behaviors." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/19754.

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Software systems will eventually contribute to their own maintenance using implementations of self-awareness. Understanding how to specify, model, and implement software with a sense of self is a daunting problem. This research draws inspiration from the automatic functioning of a gimbal---a self-righting mechanical device that supports an object and maintains the orientation of this object with respect to gravity independently of its immediate operating environment. A software gimbal exhibits a self-righting feature that provisions software with two auxiliary mechanisms: a historical mecha
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13

Längkvist, Martin. "Modeling time-series with deep networks." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-39415.

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14

Ozkan, Sule. "Modeling Elementary Students." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610168/index.pdf.

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This study aimed to explore the relationships between elementary students&rsquo<br>epistemological beliefs, learning approaches, self-regulated learning strategies, and their science achievement. In this investigation, a model of the potential associations among these variables was proposed and tested by using structural equation modeling. It was hypothesized that (a) students&rsquo<br>epistemological beliefs would directly influence their learning approaches, self-regulated learning strategies, and science achievement, (b) students&rsquo<br>adopted learning approaches and their use of self-re
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Nori, Nozomi. "Machine Learning Approaches for Personalized Clinical Risk Modeling." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225729.

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16

Wilson, Christopher R. "Transformation Learning: Modeling Transferable Transformations In High-Dimensional Data." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2334.

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The goal of learning transfer is to apply knowledge gained from one problem to a separate related problem. Transformation learning is a proposed approach to computational learning transfer that focuses on modeling high-level transformations that are well suited for transfer. By using a high-level representation of transferable data, transformation learning facilitates both shallow transfer (intra-domain) and deep transfer (inter-domain) scenarios. Transformations can be discovered in data using manifold learning to order data instances according to the transformations they represent. For high-
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17

Skalic, Miha 1990. "Deep learning for drug design : modeling molecular shapes." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667503.

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Designing novel drugs is a complex process which requires finding molecules in a vast chemical space that bind to a specific biomolecular target and have favorable physio-chemical properties. Machine learning methods can leverage previous data and use it for new predictions helping the processes of selection of molecule candidate without relying exclusively on experiments. Particularly, deep learning can be applied to extract complex patterns from simple representations. In this work we leverage deep learning to extract patterns from three-dimensional representations of molecules. We apply cl
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18

Li, Bin. "Statistical learning and predictive modeling in data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155058111.

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19

Bontempi, Gianluca. "Local learning techniques for modeling, prediction and control." Doctoral thesis, Universite Libre de Bruxelles, 2000. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211823.

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20

Allert, Heidrun [Verfasser]. "Modeling coherent social systems for learning / Heidrun Allert." Hannover : Technische Informationsbibliothek (TIB), 2005. http://d-nb.info/1229458298/34.

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21

Nguyen, Mai. "Machine Learning Algorithmsfor Regression Modeling in Private Insurance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234857.

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This thesis is focused on the Occupational Pension, an important part of the retiree’s total pension. It is paid by private insurance companies and determined by an annuity divisor. Regression modeling of the annuity divisor is done by using the monthly paid pension as a response and a set of 24 explanatory variables e.g. the expected remaining lifetime and advance interest rate. Two machine learning algorithms, artificial neural networks (ANN) and support vector machines for regression (SVR) are considered in detail. Specifically, different transfer functions for ANN are studied as well as th
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22

Josefsson, Alexandra. "Modeling an Embedded Climate System Using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290676.

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Recent advancements in processing power, storage capabilities, and availability of data, has led to improvements in many applications through the use of machine learning. Using machine learning in control systems was first suggested in the 1990s, but is more recently being implemented. In this thesis, an embedded climate system, which is a type of control system, will be looked at. The ways in which machine learning can be used to replicate portions of the climate system is looked at. Deep Belief Networks are the machine learning models of choice. Firstly, the functionality of a PID controller
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23

Mohanna, Mahmoud. "Using knowledge engineering for Modeling Mobile Learning Systems." Doctoral thesis, Université Laval, 2015. http://hdl.handle.net/20.500.11794/25533.

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En éducation, on constate un besoin de mettre à profit l’évolution continue des technologies de l'information et des communications pour développer de nouvelles applications. Ainsi, un nouveau type d'apprentissage à distance a été créé. Il s'agit de l'apprentissage mobile qui peut fournir aux apprenants l'autonomie d'apprentissage maximale. De plus, il offre aux autres acteurs du processus d'apprentissage, comme les enseignants et les gestionnaires, des méthodes plus souples d'enseignement et de gestion. Malgré sa croissance rapide, il existe de nombreux problèmes qui doivent être résolus afin
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24

Shao, Yuanlong. "Learning Sparse Recurrent Neural Networks in Language Modeling." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398942373.

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25

Pagliarini, Silvia. "Modeling the neural network responsible for song learning." Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0107.

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Pendant la première période de leur vie, les bébés et les jeunes oiseaux présentent des phases de développement vocal comparables : ils écoutent d'abord leurs parents/tuteurs afin de construire une représentation neurale du stimulus auditif perçu, puis ils commencent à produire des sons qui se rapprochent progressivement du chant de leur tuteur. Cette phase d'apprentissage est appelée la phase sensorimotrice et se caractérise par la présence de babillage. Elle se termine lorsque le chant se cristallise, c'est-à-dire lorsqu'il devient semblable à celui produit par les adultes.Il y a des similit
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Cribier-Delande, Perrine. "Contexts and user modeling through disentangled representations learning." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS407.

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Les récents succès, parfois très médiatisés, de l’apprentissage profond ont attiré beaucoup d'attention sur le domaine. Sa force réside dans sa capacité à apprendre des représentations d’objets complexes. Pour Renault, obtenir une représentation de conducteurs est un objectif à long terme, identifié depuis longtemps. Cela lui permettrait de mieux comprendre comment ses produits sont utilisés. Renault possède une grande connaissance de la voiture et des données qu’elle utilise et produit. Ces données sont presque entièrement contenues dans le CAN. Cependant, le CAN ne contient que le fonctionne
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Hansen, Joshua Aaron. "Probabilistic Explicit Topic Modeling." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/4027.

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Latent Dirichlet Allocation (LDA) is widely used for automatic discovery of latent topics in document corpora. However, output from analysis using an LDA topic model suffers from a lack of identifiability between topics not only across corpora, but across runs of the algorithm. The output is also isolated from enriching information from knowledge sources such as Wikipedia and is difficult for humans to interpret due to a lack of meaningful topic labels. This thesis introduces two methods for probabilistic explicit topic modeling that address these issues: Latent Dirichlet Allocation with Stati
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28

Zhang, Xingxing. "Natural language generation as neural sequence learning and beyond." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28930.

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Natural Language Generation (NLG) is the task of generating natural language (e.g., English sentences) from machine readable input. In the past few years, deep neural networks have received great attention from the natural language processing community due to impressive performance across different tasks. This thesis addresses NLG problems with deep neural networks from two different modeling views. Under the first view, natural language sentences are modelled as sequences of words, which greatly simplifies their representation and allows us to apply classic sequence modelling neural networks
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Maier, Michelle Filomena. "Examining Preschoolers' Trajectories of Individual Learning Behaviors: The Influence of Approaches to Learning on School Readiness." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/488.

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This study integrated variable- and child-centered techniques to investigate trajectories of four learning behaviors (initiative, persistence, planning, and problem-solving flexibility) and their influence on Head Start preschoolers' academic school readiness. Variable-centered findings revealed differential, quadratic growth trajectories for each of the four learning behaviors. However, where children began the year (intercept), how much they changed across the year (slope), and how much their rate of change changed across the year (quadratic) differed depending on the learning behavior. I
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30

Keneshloo, Yaser. "Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91910.

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Today's news agencies are moving from traditional journalism, where publishing just a few news articles per day was sufficient, to modern content generation mechanisms, which create more than thousands of news pieces every day. With the growth of these modern news agencies comes the arduous task of properly handling this massive amount of data that is generated for each news article. Therefore, news agencies are constantly seeking solutions to facilitate and automate some of the tasks that have been previously done by humans. In this dissertation, we focus on some of these problems and provide
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31

Froicu, Dragos Vasile. "Modeling and learning-based calibration of residual current devices." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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An important step in the manufacturing process of residual current devices consists of their calibration. The latter is a time-consuming procedure necessary for the proper operation of these devices. The main goal of this document is to propose a solution to increase the efficiency of the calibration workstations, by reducing the overall time of this manufacturing step. To successfully achieve this goal, a much more accurate model is needed compared to the one being used nowadays. The system under study is dominated by a huge amount of uncertainty. This is due to the high number of parameters
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32

Aguilar, Eduardo. "Deep Learning and Uncertainty Modeling in Visual Food Analysis." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670751.

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Several computer vision approaches have been proposed for tackling food analysis problems, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. However, high food ambiguity, interclass variability and intra-class similarity define a real challenge for the Deep learning and Computer Vision algorithms. With the advent of Convolutional Neural Networks, the complex problem of visual food analysis has experienced significant improvement. Despite this, for real applications, where thousands of foods must be analyzed and rec
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33

Hassani, Kaveh. "Commonsense Knowledge for 3D Modeling: A Machine Learning Approach." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36165.

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Common‒sense knowledge is a collection of non‒expert and agreed‒upon facts and information about the world shared among most people past early childhood based on their experiences. It includes uses of objects, their properties, parts and materials, their locations, spatial arrangements among them; location and duration of events; arguments, preconditions and effects of actions; urges and emotions of people, etc. In creating 3D worlds and especially text‒ to‒scene and text‒to‒animation systems, this knowledge is essential to eliminate the tedious and low‒level tasks, thus allowing users to focu
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34

Chan, Chun-leung, and 陳振良. "The learning of science concepts through simulation and modeling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B30409160.

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35

Koulouris, Alexandros. "Multiresolution learning in nonlinear dynamic process modeling and control." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11376.

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Orwant, Jon. "Doppelgänger goes to school : machine learning for user modeling." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/29070.

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37

Silva, Thiago Christiano. "Machine learning in complex networks: modeling, analysis, and applications." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19042013-104641/.

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Machine learning is evidenced as a research area with the main purpose of developing computational methods that are capable of learning with their previously acquired experiences. Although a large amount of machine learning techniques has been proposed and successfully applied in real systems, there are still many challenging issues, which need be addressed. In the last years, an increasing interest in techniques based on complex networks (large-scale graphs with nontrivial connection patterns) has been verified. This emergence is explained by the inherent advantages provided by the complex ne
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Lindblad, Simon. "Labeling Clinical Reports with Active Learning and Topic Modeling." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148463.

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Supervised machine learning models require a labeled data set of high quality in order to perform well. Available text data often exists in abundance, but it is usually not labeled. Labeling text data is a time consuming process, especially in the case where multiple labels can be assigned to a single text document. The purpose of this thesis was to make the labeling process of clinical reports as effective and effortless as possible by evaluating different multi-label active learning strategies. The goal of the strategies was to reduce the number of labeled documents a model needs, and increa
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Cubuk, Cankut. "Modeling Functional Modules Using Statistical and Machine Learning Methods." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/156175.

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[ES] La comprensión de los aspectos de la funcionalidad de las células que cuentan para los mecanismos de las enfermedades es el mayor reto de la medicina personalizada. A pesar de la disponibilidad creciente de los datos de genómica y transcriptómica, sigue existiendo una notable brecha entre la detección de las perturbaciones en la expresión de genes y la comprensión de su contribución en los mecanismos moleculares que últimamente tienen relación importante con el fenotipo estudiado. A lo largo de la última década, distintos modelos computacionales y matemáticos se han propuesto para el anál
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Rao, Rashmi Jayathirtha. "Modeling learning behaviour and cognitive bias from web logs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492560600002105.

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Liljefors, Felix. "Time dependent modeling of turbocharger failure using machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287451.

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Data-driven predictive vehicle maintenance can in principle reduce the risk of costly breakdowns, damaged cargo, and increased emissions due to faulty components. However, implementing a cost efficient predictive maintenance policy is far from trivial. This thesis presents a machine learning-based method for predicting turbocharger component failure in heavy-duty trucks, called Survival-PLSTM. Survival-PLSTM uses a recurrent neural-network to model time-series data of a vehicle’s operational history, and outputs an approximated cumulative hazard function. The method addresses a number of chall
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Chong, Linsen. "Modeling Naturalistic Driver Behavior in Traffic Using Machine Learning." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/76834.

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This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied to simulate driver naturalistic driving behavior including risk-taking behavior during an incident and lateral evasive behavior which have not yet been captured in existing literature. Two special machi
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Karakurt, Murat Özdemir Serhan. "Data driven modeling using reinforcement learning in autonomous agents/." [s.l.]: [s.n.], 2003. http://library.iyte.edu.tr/tezler/master/makinamuh/T000276.pdf.

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Chan, Chun-leung. "The learning of science concepts through simulation and modeling /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25147985.

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45

Wu, Anjian M. B. A. Sloan School of Management. "Performance modeling of human-machine interfaces using machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122599.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 70-71).<br>As the popularity of online retail expands, world-class electronic commerce (e-commerce) businesses are increasingly adopting collaborative robotics and I
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Chavez-Gehrig, Arturo. "Modeling correlations in clinical trial outcomes using machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123075.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 69-71).<br>This thesis explores the problem of characterizing the covariance of clinical trial outcomes using drug and trial features. The binary nature of FDA approvals makes drug development risky, but approaches in finance theory could better manage that risk, allowing more high potential drugs to be developed. To apply these methods confidently, it is necessary to understand the covaria
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Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

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The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investiga
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48

Hu, Shuwen. "Statistical modeling and machine learning in longitudinal data analysis." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211253/1/Shuwen_Hu_Thesis.pdf.

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This thesis mainly concerns the statistical modelling and machine learning methods for the analysis of longitudinal data. As a contribution to this area, this thesis provides theoretical discussion and empirical illustrations of longitudinal data analysis. The first contribution is developing methods to obtain robust and efficient variance estimators when the cluster size is large. The second one is comparing a traditional parametric approach, the linear mixed model with machine learning methods in longitudinal data analysis. The last one is extracting new features to improve sheep behaviour c
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Del, Testa Davide. "Stochastic Optimization and Machine Learning Modeling for Wireless Networking." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3424825.

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In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, includi
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Mehr, Éloi. "Unsupervised Learning of 3D Shape Spaces for 3D Modeling." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS566.

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Bien que les données 3D soient de plus en plus populaires, en particulier avec la démocratisation des expériences de réalité virtuelle et augmentée, il reste très difficile de manipuler une forme 3D, même pour des designers ou des experts. Partant d’une base de données d’instances 3D d’une ou plusieurs catégories d’objets, nous voulons apprendre la variété des formes plausibles en vue de développer de nouveaux outils intelligents de modélisation et d’édition 3D. Cependant, cette variété est souvent bien plus complexe comparée au domaine 2D. En effet, les surfaces 3D peuvent être représentées e
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