Tesis sobre el tema "3P model of learning"
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Jones, Catherine Toni y n/a. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning". Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030304.092316.
Texto completoJones, Catherine Toni. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366357.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Full Text
Tai, Chunming. "Undergraduate business and management students' experiences of being involved in assessment". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/9456.
Texto completoDraper, Fiona J. "Development of a Student-Centred Evaluation Framework for Environmental Vocational Education and Training Courses. Development and validation of a Student-Centred Evaluation Framework for Environmental Vocational Education and Training Courses derived from Biggs' 3P Model and Kirkpatrick's Four Levels Evaluation Model". Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5496.
Texto completoDraper, Fiona Jane. "Development of a student-centred evaluation framework for environmental vocational education and training courses : development and validation of a student-centred evaluation framework for environmental vocational education and training courses derived from Biggs' 3P Model and Kirkpatrick's Four Levels Evaluation Model". Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5496.
Texto completoHuang, Mei-hui. "Factors influencing self-directed learning readiness amongst Taiwanese nursing students". Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/20709/1/Mei-hui_Huang_Thesis.pdf.
Texto completoHuang, Mei-hui. "Factors influencing self-directed learning readiness amongst Taiwanese nursing students". Queensland University of Technology, 2008. http://eprints.qut.edu.au/20709/.
Texto completoMahadevan, Shankar. "A Learning Object Model For Electronic Learning". Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34060.
Texto completoMaster of Science
GHADIRZADEH, ALI. "LEARNING A VISUALFORWARD MODEL". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142034.
Texto completoDorfler, Viktor. "Model of learning ability". Thesis, University of Strathclyde, 2005. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=9341.
Texto completoGawande, Nitin A. "Modeling microbiological and chemical processes in municipal solid waste bioreactor development and applications of a three-phase numerical model BIOKEMOD-3P /". Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002659.
Texto completoGomes, Herman M. "Model learning in iconic vision". Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/323.
Texto completoHaussamer, Nicolai Haussamer. "Model Calibration with Machine Learning". Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29451.
Texto completoLayne, Jeffery Ray. "Fuzzy model reference learning control". Connect to resource, 1992. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1159541293.
Texto completoEne, Gloria Unoma. "A learning 'learning' model for optimised construction workforce development". Thesis, University of Central Lancashire, 2017. http://clok.uclan.ac.uk/20919/.
Texto completoBerragan, Elizabeth Anne. "Learning nursing through simulation : towards an expansive model of learning". Thesis, University of the West of England, Bristol, 2013. http://eprints.uwe.ac.uk/20107/.
Texto completoSaitas-Zarkias, Konstantinos. "Insights into Model-Agnostic Meta-Learning on Reinforcement Learning Tasks". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290903.
Texto completoMeta-Learning har fått dragkraft inom Deep Learning fältet som ett tillvägagångssätt för att bygga modeller som effektivt kan anpassa sig till nya uppgifter efter distribution. I motsats till konventionella maskininlärnings metoder som är tränade för en specifik uppgift (t.ex. bild klassificering på en uppsättning klasser), så metatränas meta-learning metoder över flera uppgifter (t.ex. bild klassificering över flera uppsättningar av klasser). Deras slutmål är att lära sig att lösa osedda uppgifter med bara några få prover. En av de mest kända metoderna inom området är Model-Agnostic Meta-Learning (MAML). Syftet med denna avhandling är att komplettera den senaste relevanta forskningen med nya observationer avseende MAML: s kapacitet, begränsningar och nätverksdynamik. För detta ändamål utfördes experiment på metaförstärkningslärande riktmärke Meta-World. Dessutom gjordes en jämförelse med en ny variant av MAML, kallad Almost No Inner Loop (ANIL), som gav insikter om förändringarna i nätverkets representation under anpassning (metatestning). Resultaten av denna studie indikerar att MAML kan överträffa baslinjerna för det utmanande Meta-Worldriktmärket men visar små tecken på faktisk ”snabb inlärning” under metatestning, vilket stödjer hypotesen att den återanvänder funktioner som den lärt sig under metaträning.
Brock, David E. "Group therapy : an interpersonal learning model". Thesis, University of Surrey, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329423.
Texto completoDoshi, Finale (Finale P. ). "Efficient model learning for dialog management". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40325.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 118-122).
Partially Observable Markov Decision Processes (POMDPs) have succeeded in many planning domains because they can optimally trade between actions that will increase an agent's knowledge about its environment and actions that will increase an agent's reward. However, POMDPs are defined with a large number of parameters which are difficult to specify from domain knowledge, and gathering enough data to specify the parameters a priori may be expensive. This work develops several efficient algorithms for learning the POMDP parameters online and demonstrates them on dialog manager for a robotic wheelchair. In particular, we show how a combination of specialized queries ("meta-actions") can enable us to create a robust dialog manager that avoids the pitfalls in other POMDP-learning approaches. The dialog manager's ability to reason about its uncertainty -- and take advantage of low-risk opportunities to reduce that uncertainty -- leads to more robust policy learning.
by Final Doshi.
S.M.
Bizonova, Zuzana. "Model driven e-learning platform integration". Evry, Télécom & Management SudParis, 2008. http://www.theses.fr/2008TELE0011.
Texto completoIn the recent years, e-learning gained popularity among educational institutions as well as enterprises. As the result of that many commercial or open-source Learning Management Systems (LMS) were developed to manage online courses. However, while the usage of these systems gained recognition and acceptance amongst institutions, a new category of problems arose that needs to be solved: because of multiplicity of platforms and approaches used for various systems implementation, it became increasingly difficult to exchange pieces of information among those systems. Applications and their data become isolated - a clear economical concern for the future of these technologies. The present study describes a method, based on Model Driven Architecture (MDA), for integrating approaches of candidate LMS systems into a generalized architectural framework. The framework makes use of standards for description of data and metadata like learning materials (IEEE LOM, IEEE PAPI), student information (IMS LIP) or learning design (IMS LD). This platform-independent framework can be used for automatic migration of data between various e-learning platforms
Počas posledných desiatich rokov si e-learning získal popularitu medzi vzdelávacími inštitúciami po celom svete. Výsledkom tohto trendu bola tvorba mnohých Learning Management Systémov na správu e-learningových kurzov. Mnohé z týchto systémov získavajú stále väčší počet používateľov avšak začínajú sa objavovať nové problémy spojené s ich používaním. Množstvo rôznorodých systémov a platforiem spôsobuje problémy pri zdieľaní dát medzi nimi. Tieto aplikácie a ich dáta ostávajú navzájom medzi sebou v izolácii, čo však môže spôsobiť vážne ekonomické problémy a ohroziť budúcnosť týchto technológií. Pre lepšie pochopenie, ide tu o zdieľanie takých dát ako sú vzdelávacie materiály či záznamy a výsledky jednotlivých študentov. Vytvorenie kvalitného materiálu je časovo aj myšlienkovo náročný process. Ak nie je možné zdieľať tieto dáta medzi systémami, znamená to, že je problematické ich znovuvyužitie v inej platforme. V tejto situácii je potrebné opakovane vytvárať rovnaké druhy informácií pre rozličné systémy. Táto štúdia opisuje metódu založenú na Modelovo orientovanej architektúre (MDA), ktorá integruje prístupy rozličných pozorovaných LMS systémov do zovšeobecného architektonického rámca, ktorý využíva štandardy pre popis dát a metadát ako napríklad IEEE LOM, IMS QTI či IEEE PAPI alebo IMS LD. Tento platformovo nezávislý rámec nám umožní automaticke zdieľanie rozličných druhov dát medzi e-learningovými platformami
Zhao, Yajing. "Chaotic Model Prediction with Machine Learning". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.
Texto completoNoelle, David Charles. "A connectionist model of instructed learning /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9811797.
Texto completoGriffiths, Michael Edward. "Improving the asynchronous video learning model /". Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3518.pdf.
Texto completoGriffiths, Michael E. "Improving the Asynchronous Video Learning Model". BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2048.
Texto completoNitesh, Varma Rudraraju Nitesh y Boyanapally Varun Varun. "Data Quality Model for Machine Learning". Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18498.
Texto completoChen, Yang. "Improving student model for individualized learning". Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066655/document.
Texto completoComputer-based educational environments, like Intelligent Tutoring Systems (ITSs), have been used to enhance human learning. These environments aim at increasing student achievement by providing individualized instructions. It has been recognized that individualized learning is more effective than the conventional learning. Student models which are used to capture student knowledge underlie the individualized learning. In recent decades, various competing student models have been proposed. However, some diagnostic information in student behaviors is usually ignored by these models. Furthermore, to individualize learning paths, student models should capture prerequisite structures of fine-grained skills. However, acquiring skill structures requires much knowledge engineering effort. We improve student models for individualized learning with respect to the two aspects. On one hand, in order to improve the diagnostic ability of a student model, we introduce the diagnostic feature—student error patterns. To deal with the noise in student performance data, we extend a sound probabilistic model to incorporate erroneous responses. The results show that the diagnostic feature improves the prediction accuracy of student models. On the other hand, we target on discovering prerequisite structures of skills from student performance data. It is a challenging task, since student knowledge of a skill is a latent variable. We propose a two-phase method to discover skill structure from noisy observations. Our method is validated on simulated data and real data. In addition, we verify that prerequisite structures of skills can improve the accuracy of a student model
Grappin, Edwin. "Model Averaging in Large Scale Learning". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLG001/document.
Texto completoThis thesis explores properties of estimations procedures related to aggregation in the problem of high-dimensional regression in a sparse setting. The exponentially weighted aggregate (EWA) is well studied in the literature. It benefits from strong results in fixed and random designs with a PAC-Bayesian approach. However, little is known about the properties of the EWA with Laplace prior. Chapter 2 analyses the statistical behaviour of the prediction loss of the EWA with Laplace prior in the fixed design setting. Sharp oracle inequalities which generalize the properties of the Lasso to a larger family of estimators are established. These results also bridge the gap from the Lasso to the Bayesian Lasso. Chapter 3 introduces an adjusted Langevin Monte Carlo sampling method that approximates the EWA with Laplace prior in an explicit finite number of iterations for any targeted accuracy. Chapter 4 explores the statisctical behaviour of adjusted versions of the Lasso for the transductive and semi-supervised learning task in the random design setting
Wang, Jiahao. "Vehicular Traffic Flow Prediction Model Using Machine Learning-Based Model". Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42288.
Texto completoNguyen, Tri. "Learning tensions : a multilevel model of organisational learning : an empirical study". Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/425925/.
Texto completoWorrall, Lisa Jayne Rosalind. "Model of metacognition in lifelong e-learning". Thesis, University of Salford, 2005. http://usir.salford.ac.uk/26967/.
Texto completoAlexander, Miranda Abhilash. "Spectral factor model for time series learning". Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209812.
Texto completomassive amounts of streaming data.
In many applications, data is collected for modeling the processes. The process model is hoped to drive objectives such as decision support, data visualization, business intelligence, automation and control, pattern recognition and classification, etc. However, we face significant challenges in data-driven modeling of processes. Apart from the errors, outliers and noise in the data measurements, the main challenge is due to a large dimensionality, which is the number of variables each data sample measures. The samples often form a long temporal sequence called a multivariate time series where any one sample is influenced by the others.
We wish to build a model that will ensure robust generation, reviewing, and representation of new multivariate time series that are consistent with the underlying process.
In this thesis, we adopt a modeling framework to extract characteristics from multivariate time series that correspond to dynamic variation-covariation common to the measured variables across all the samples. Those characteristics of a multivariate time series are named its 'commonalities' and a suitable measure for them is defined. What makes the multivariate time series model versatile is the assumption regarding the existence of a latent time series of known or presumed characteristics and much lower dimensionality than the measured time series; the result is the well-known 'dynamic factor model'.
Original variants of existing methods for estimating the dynamic factor model are developed: The estimation is performed using the frequency-domain equivalent of the dynamic factor model named the 'spectral factor model'. To estimate the spectral factor model, ideas are sought from the asymptotic theory of spectral estimates. This theory is used to attain a probabilistic formulation, which provides maximum likelihood estimates for the spectral factor model parameters. Then, maximum likelihood parameters are developed with all the analysis entirely in the spectral-domain such that the dynamically transformed latent time series inherits the commonalities maximally.
The main contribution of this thesis is a learning framework using the spectral factor model. We term learning as the ability of a computational model of a process to robustly characterize the data the process generates for purposes of pattern matching, classification and prediction. Hence, the spectral factor model could be claimed to have learned a multivariate time series if the latent time series when dynamically transformed extracts the commonalities reliably and maximally. The spectral factor model will be used for mainly two multivariate time series learning applications: First, real-world streaming datasets obtained from various processes are to be classified; in this exercise, human brain magnetoencephalography signals obtained during various cognitive and physical tasks are classified. Second, the commonalities are put to test by asking for reliable prediction of a multivariate time series given its past evolution; share prices in a portfolio are forecasted as part of this challenge.
For both spectral factor modeling and learning, an analytical solution as well as an iterative solution are developed. While the analytical solution is based on low-rank approximation of the spectral density function, the iterative solution is based on the expectation-maximization algorithm. For the human brain signal classification exercise, a strategy for comparing similarities between the commonalities for various classes of multivariate time series processes is developed. For the share price prediction problem, a vector autoregressive model whose parameters are enriched with the maximum likelihood commonalities is designed. In both these learning problems, the spectral factor model gives commendable performance with respect to competing approaches.
Les processus informatisés actuels génèrent des quantités massives de flux de données. Dans nombre d'applications, ces flux de données sont collectées en vue de modéliser les processus. Les modèles de processus obtenus ont pour but la réalisation d'objectifs tels que l'aide à la décision, la visualisation de données, l'informatique décisionnelle, l'automatisation et le contrôle, la reconnaissance de formes et la classification, etc. La modélisation de processus sur la base de données implique cependant de faire face à d’importants défis. Outre les erreurs, les données aberrantes et le bruit, le principal défi provient de la large dimensionnalité, i.e. du nombre de variables dans chaque échantillon de données mesurées. Les échantillons forment souvent une longue séquence temporelle appelée série temporelle multivariée, où chaque échantillon est influencé par les autres. Notre objectif est de construire un modèle robuste qui garantisse la génération, la révision et la représentation de nouvelles séries temporelles multivariées cohérentes avec le processus sous-jacent.
Dans cette thèse, nous adoptons un cadre de modélisation capable d’extraire, à partir de séries temporelles multivariées, des caractéristiques correspondant à des variations - covariations dynamiques communes aux variables mesurées dans tous les échantillons. Ces caractéristiques sont appelées «points communs» et une mesure qui leur est appropriée est définie. Ce qui rend le modèle de séries temporelles multivariées polyvalent est l'hypothèse relative à l'existence de séries temporelles latentes de caractéristiques connues ou présumées et de dimensionnalité beaucoup plus faible que les séries temporelles mesurées; le résultat est le bien connu «modèle factoriel dynamique». Des variantes originales de méthodes existantes pour estimer le modèle factoriel dynamique sont développées :l'estimation est réalisée en utilisant l'équivalent du modèle factoriel dynamique au niveau du domaine de fréquence, désigné comme le «modèle factoriel spectral». Pour estimer le modèle factoriel spectral, nous nous basons sur des idées relatives à la théorie des estimations spectrales. Cette théorie est utilisée pour aboutir à une formulation probabiliste, qui fournit des estimations de probabilité maximale pour les paramètres du modèle factoriel spectral. Des paramètres de probabilité maximale sont alors développés, en plaçant notre analyse entièrement dans le domaine spectral, de façon à ce que les séries temporelles latentes transformées dynamiquement héritent au maximum des points communs.
La principale contribution de cette thèse consiste en un cadre d'apprentissage utilisant le modèle factoriel spectral. Nous désignons par apprentissage la capacité d'un modèle de processus à caractériser de façon robuste les données générées par le processus à des fins de filtrage par motif, classification et prédiction. Dans ce contexte, le modèle factoriel spectral est considéré comme ayant appris une série temporelle multivariée si la série temporelle latente, une fois dynamiquement transformée, permet d'extraire les points communs de façon fiable et maximale. Le modèle factoriel spectral sera utilisé principalement pour deux applications d'apprentissage de séries multivariées :en premier lieu, des ensembles de données sous forme de flux venant de différents processus du monde réel doivent être classifiés; lors de cet exercice, la classification porte sur des signaux magnétoencéphalographiques obtenus chez l'homme au cours de différentes tâches physiques et cognitives; en second lieu, les points communs obtenus sont testés en demandant une prédiction fiable d'une série temporelle multivariée étant donnée l'évolution passée; les prix d'un portefeuille d'actions sont prédits dans le cadre de ce défi.
À la fois pour la modélisation et pour l'apprentissage factoriel spectral, une solution analytique aussi bien qu'une solution itérative sont développées. Tandis que la solution analytique est basée sur une approximation de rang inférieur de la fonction de densité spectrale, la solution itérative est basée, quant à elle, sur l'algorithme de maximisation des attentes. Pour l'exercice de classification des signaux magnétoencéphalographiques humains, une stratégie de comparaison des similitudes entre les points communs des différentes classes de processus de séries temporelles multivariées est développée. Pour le problème de prédiction des prix des actions, un modèle vectoriel autorégressif dont les paramètres sont enrichis avec les points communs de probabilité maximale est conçu. Dans ces deux problèmes d’apprentissage, le modèle factoriel spectral atteint des performances louables en regard d’approches concurrentes.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Jasso, Hector. "A reinforcement learning model of gaze following". Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3259369.
Texto completoTitle from first page of PDF file (viewed June 22, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 104-116).
Cora, Vlad M. "Model-based active learning in hierarchical policies". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/737.
Texto completoSchmidt, Mark. "Graphical model structure learning using L₁-regularization". Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27277.
Texto completoLandelius, Tomas. "Reinforcement Learning and Distributed Local Model Synthesis". Doctoral thesis, Linköpings universitet, Bildbehandling, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54348.
Texto completoKubilinskienė, Svetlana. "Extended metadata model for digital learning resources". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120402_093822-17816.
Texto completoPagrindinis informacinių technologijų (IT) naudojimo mokymuisi tikslas – didinti mokymosi kokybę ir efektyvumą, lengvinti besimokančiojo ir mokytojo darbą. Galima išskirti dvi IT taikymo ugdymui kryptis: 1) kai naudojant IT siekiama gerinti tradicinius metodus 2) kai sukuriami nauji metodai, kuriuos taikyti įmanoma tik naudojant IT. Abiem atvejais svarbus mokytojų gerosios patirties dalijimasis, mokymosi metodų įvaldymas. Disertacinis darbas skirtas metodinių išteklių ir mokymosi metodų naudojimo problemoms, kylančioms dėl informacijos nepakankamumo mokymosi objektų (MO) metaduomenų saugyklose, spręsti. Išanalizuoti ir palyginti pagrindiniai MO metaduomenų standartų modeliai, naudojami skaitmeniniams mokymosi ištekliams formaliuoju būdu aprašyti. Ištirti MO metaduomenų standartų taikymo modelių sudarymo moksliniai ir praktiniai principai. Išanalizuoti turinio MO kūrimo modeliai, kurie užtikrina MO suderinamumą. Atliktas empirinis tyrimas leido nustatyti tolesnę tyrimo kryptį ir turėjo įtakos išplėsto MO metaduomenų taikomojo modelio kūrimui. Metaduomenų taikomojo modelio projektavimo procesą sudaro šie etapai: 1) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų elementų aibių išskyrimas; 2) valdomųjų žodynų, skirtų metaduomenų elementams aprašyti, formavimas siekiant užtikrinti metaduomenų suderinamumą; 3) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų lyginamoji analizė; 4) išplėsto MO metaduomenų modelio išbaigimas ir diegimas... [toliau žr. visą tekstą]
Makris, Dimitrios. "Learning an activity-based semantic scene model". Thesis, City University London, 2004. http://eprints.kingston.ac.uk/7781/.
Texto completoMohd, Alwi Najwa Hayaati. "E-learning stakeholders information security vulnerability model". Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7387.
Texto completoTudevdagva, Uranchimeg. "Structure Oriented Evaluation Model for E-Learning". Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-146901.
Texto completoFollett, Stephen James. "A computational model of learning in Go". Thesis, University of South Wales, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343412.
Texto completoRizzi, Raymundo Caroline. "SAFEL : a Situation-Aware Fear Learning model". Thesis, University of Kent, 2017. https://kar.kent.ac.uk/65705/.
Texto completoGilja, Vikash. "Learning and applying model-based visual context". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33139.
Texto completoIncludes bibliographical references (p. 53).
I believe that context's ability to reduce the ambiguity of an input signal makes it a vital constraint for understanding the real world. I specifically examine the role of context in vision and how a model-based approach can aid visual search and recognition. Through the implementation of a system capable of learning visual context models from an image database, I demonstrate the utility of the model-based approach. The system is capable of learning models for "water-horizon scenes" and "suburban street scenes" from a database of 745 images.
by Vikash Gilja.
M.Eng.
Weintraub, Ben Julian. "Learning control applied to a model helicopter". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/49921.
Texto completoEser, Ercan. "Criminality-oriented terrorist learning : an interactive model". Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/38811/.
Texto completoWongse-Ek, Woraluck. "Towards a trust model in e-learning". Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/400246/.
Texto completoBengtsson, Ivar. "Autonomous Overtaking with Learning Model Predictive Control". Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.
Texto completoVi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.
Jensen, Sara Lyn. "Learning Russian Case Endings Through Model Sentences". Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2000.pdf.
Texto completoMeng, Zhaoxin. "A deep learning model for scene recognition". Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.
Texto completoWarner-Metzger, C., B. C. Reed, John Paul Abner, Janet Todd y Michele R. Moser. "PCIT training: Applying a Learning Collaborative Model". Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etsu-works/4978.
Texto completoRoberts, Irma. "Performance management : a connected professional learning model". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2006. https://ro.ecu.edu.au/theses/324.
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