Дисертації з теми "Operator Learning"
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Tummaluri, Raghuram R. "Operator Assignment in Labor Intensive Cells Considering Operation Time Based Skill Levels, Learning and Forgetting." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1126900571.
Повний текст джерелаKienzle, Wolf. "Learning an interest operator from human eye movements." Berlin Logos-Verl, 2008. http://d-nb.info/990541908/04.
Повний текст джерелаSchrödl, Stefan J. "Operator valued reproducing kernels and their application in approximation and statistical learning." Aachen Shaker, 2009. http://d-nb.info/99654559X/04.
Повний текст джерелаHuusari, Riikka. "Kernel learning for structured data : a study on learning operator - and scalar - valued kernels for multi-view and multi-task learning problems." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0312.
Повний текст джерелаNowadays datasets with non-standard structures are more and more common. Examples include the already well-known multi-task framework where each data sample is associated with multiple output labels, as well as the multi-view learning paradigm, in which each data sample can be seen to contain numerous descriptions. To obtain a good performance in tasks like these, it is important to model the interactions present in the views or output variables well.Kernel methods offer a justified and elegant way to solve many machine learning problems. Operator-valued kernels, which generalize the well-known scalar-valued kernels, have gained attention recently as a way to learn vector-valued functions. The choice of a good kernel function plays crucial role for the success on the learning task.This thesis offers kernel learning as a solution for various machine learning problems. Chapters two and three investigate learning the data interactions with multi-view data. In the first of these, the focus is in supervised inductive learning and the interactions are modeled with operator-valued kernels. Chapter three tackles multi-view data and kernel learning in unsupervised context and proposes a scalar-valued kernel learning method for completing missing data in kernel matrices of a multi-view problem. In the last chapter we turn from multi-view to multi-output learning, and return to the supervised inductive learning paradigm. We propose a method for learning inseparable operator-valued kernels that model interactions between inputs and multiple output variables
Montagner, Igor dos Santos. "W-operator learning using linear models for both gray-level and binary inputs." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-21082017-111455/.
Повний текст джерелаProcessamento de imagens pode ser usado para resolver problemas em diversas áreas, como imagens médicas, processamento de documentos e segmentação de objetos. Operadores de imagens normalmente são construídos combinando diversos operadores elementares e ajustando seus parâmetros. Uma abordagem alternativa é a estimação de operadores de imagens a partir de pares de exemplos contendo uma imagem de entrada e o resultado esperado. Restringindo os operadores considerados para o que são invariantes à translação e localmente definidos ($W$-operadores), podemos aplicar técnicas de Aprendizagem de Máquina para estimá-los. O formato que define quais vizinhos são usadas é chamado de janela. $W$-operadores treinados com janelas grandes frequentemente tem problemas de generalização, pois necessitam de grandes conjuntos de treinamento. Este problema é ainda mais grave ao treinar operadores em níveis de cinza. Apesar de técnicas como o projeto dois níveis, que combina a saída de diversos operadores treinados com janelas menores, mitigar em parte estes problemas, uma determinação de parâmetros complexa é necessária. Neste trabalho apresentamos duas técnicas que permitem o treinamento de operadores usando janelas grandes. A primeira, KA, é baseada em Máquinas de Suporte Vetorial (SVM) e utiliza técnicas de aproximação de kernels para realizar o treinamento de $W$-operadores. Uma escolha adequada de kernels permite o treinamento de operadores níveis de cinza e binários. A segunda técnica, NILC, permite a criação automática de combinações de operadores de imagens. Este método utiliza uma técnica de otimização específica para casos em que o número de características é muito grande. Ambos métodos obtiveram resultados competitivos com algoritmos da literatura em dois domínio de aplicação diferentes. O primeiro, Staff Removal, é um processamento de documentos binários frequente em sistemas de reconhecimento ótico de partituras. O segundo é um problema de segmentação de vasos sanguíneos em imagens em níveis de cinza.
Alhawari, Omar I. "Operator Assignment Decisions in a Highly Dynamic Cellular Environment." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1221596218.
Повний текст джерелаSchrödl, Stefan J. [Verfasser]. "Operator-valued Reproducing Kernels and Their Application in Approximation and Statistical Learning / Stefan J Schrödl." Aachen : Shaker, 2009. http://d-nb.info/1159835454/34.
Повний текст джерелаWörmann, Julian [Verfasser], Martin [Akademischer Betreuer] Kleinsteuber, Martin [Gutachter] Kleinsteuber, and Walter [Gutachter] Stechele. "Structured Co-sparse Analysis Operator Learning for Inverse Problems in Imaging / Julian Wörmann ; Gutachter: Martin Kleinsteuber, Walter Stechele ; Betreuer: Martin Kleinsteuber." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1205069437/34.
Повний текст джерелаTamascelli, Nicola. "A Machine Learning Approach to Predict Chattering Alarms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Знайти повний текст джерелаLee, Ji Hyun. "Development of a Tool to Assist the Nuclear Power Plant Operator in Declaring a State of Emergency Based on the Use of Dynamic Event Trees and Deep Learning Tools." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543069550674204.
Повний текст джерелаSyben, Christopher [Verfasser], Andreas [Akademischer Betreuer] Maier, Andreas [Gutachter] Maier, and Adam [Gutachter] Wang. "Known Operator Learning for a Hybrid Magnetic Resonance/X-ray Imaging Acquisition Scheme / Christopher Syben ; Gutachter: Andreas Maier, Adam Wang ; Betreuer: Andreas Maier." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2021. http://d-nb.info/1237499151/34.
Повний текст джерелаSanguanpuak, T. (Tachporn). "Radio resource sharing with edge caching for multi-operator in large cellular networks." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526221564.
Повний текст джерелаTiivistelmä Tämän väitöskirjan tavoitteena on tuottaa uusia paradigmoja radioresurssien jakoon, mukaan lukien virtualisoidut välimuisti-kykenevät suuret matkapuhelinverkot matkapuhelinoperaattoreille. Näiden kaltaisissa verkoissa operaattorit vuokraavat radioresursseja infrastruktuuritoimittajalta (InP, infrastructure provider) asiakkaiden tarpeisiin. Toimintakulujen karsiminen ja samanaikainen olemassa olevien verkkoresurssien hyötykäytön huomattava kasvattaminen johtaa paradigmaan, jossa operaattorit jakavat infrastruktuurinsa keskenään. Tämän vuoksi työssä tutkitaan teoreettisia stokastiseen geometriaan perustuvia malleja spektrin ja infrastruktuurin jakamiseksi suurissa soluverkoissa. Työn ensimmäisessä osassa tutkitaan ei-ortogonaalista monioperaattori-allokaatioongelmaa pienissä soluverkoissa tavoitteena maksimoida verkon yleistä läpisyöttöä, joka määritellään operaattoreiden painotettuna summaläpisyötön odotusarvona. Jokaisen operaattorin oletetaan palvelevan useampaa piensolutukiasemaa (SBS, small cell base station). Työssä käytetään monelta yhdelle -vakaata sovituspeli-viitekehystä SBS:lle käyttäen Q-oppimista. Työn toisessa osassa mallinnetaan ja analysoidaan infrastruktuurin jakamista yhden ostaja-operaattorin ja monen myyjä-operaattorin tapauksessa. Operaattorien oletetaan toimivan omilla lisensoiduilla taajuuksillaan jakaen tukiasemat keskenään. Myyjän optimaalinen strategia infrastruktuurin myytävän osan suuruuden ja hinnan suhteen saavutetaan laskemalla Cournot-Nash -olipologipelin tasapainotila. Lopuksi, työssä kehitetään peli-teoreettinen viitekehys virtualisoitujen välimuistikykenevien soluverkkojen mallintamiseen ja analysointiin, missä InP:n omistama verkkoinfrastruktuuri vuokrataan ja jaetaan monen operaattorin kesken. Työssä muodostetaan Stackelberg-pelimalli, jossa InP toimii johtajana ja operaattorit seuraajina. InP pyrkii maksimoimaan voittonsa optimoimalla infrastruktuurin vuokrahintaa. Operaattori pyrkii minimoimaan infrastruktuurin hinnan minimoimalla välimuistin tiheyttä satunnaisen käyttäjän viive-ehtojen mukaisesti. Koska operaattorit jakavat vuokratun infrastruktuurin, työssä käytetään yhteistyöpeli-ajatusta, nimellisesti, Shapleyn arvoa, jakamaan kustannuksia operaatoreiden kesken
Laforgue, Pierre. "Deep kernel representation learning for complex data and reliability issues." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT006.
Повний текст джерелаThe first part of this thesis aims at exploring deep kernel architectures for complex data. One of the known keys to the success of deep learning algorithms is the ability of neural networks to extract meaningful internal representations. However, the theoretical understanding of why these compositional architectures are so successful remains limited, and deep approaches are almost restricted to vectorial data. On the other hand, kernel methods provide with functional spaces whose geometry are well studied and understood. Their complexity can be easily controlled, by the choice of kernel or penalization. In addition, vector-valued kernel methods can be used to predict kernelized data. It then allows to make predictions in complex structured spaces, as soon as a kernel can be defined on it.The deep kernel architecture we propose consists in replacing the basic neural mappings functions from vector-valued Reproducing Kernel Hilbert Spaces (vv-RKHSs). Although very different at first glance, the two functional spaces are actually very similar, and differ only by the order in which linear/nonlinear functions are applied. Apart from gaining understanding and theoretical control on layers, considering kernel mappings allows for dealing with structured data, both in input and output, broadening the applicability scope of networks. We finally expose works that ensure a finite dimensional parametrization of the model, opening the door to efficient optimization procedures for a wide range of losses.The second part of this thesis investigates alternatives to the sample mean as substitutes to the expectation in the Empirical Risk Minimization (ERM) paradigm. Indeed, ERM implicitly assumes that the empirical mean is a good estimate of the expectation. However, in many practical use cases (e.g. heavy-tailed distribution, presence of outliers, biased training data), this is not the case.The Median-of-Means (MoM) is a robust mean estimator constructed as follows: the original dataset is split into disjoint blocks, empirical means on each block are computed, and the median of these means is finally returned. We propose two extensions of MoM, both to randomized blocks and/or U-statistics, with provable guarantees. By construction, MoM-like estimators exhibit interesting robustness properties. This is further exploited by the design of robust learning strategies. The (randomized) MoM minimizers are shown to be robust to outliers, while MoM tournament procedure are extended to the pairwise setting.We close this thesis by proposing an ERM procedure tailored to the sample bias issue. If training data comes from several biased samples, computing blindly the empirical mean yields a biased estimate of the risk. Alternatively, from the knowledge of the biasing functions, it is possible to reweight observations so as to build an unbiased estimate of the test distribution. We have then derived non-asymptotic guarantees for the minimizers of the debiased risk estimate thus created. The soundness of the approach is also empirically endorsed
Arthur, Richard B. "Vision-Based Human Directed Robot Guidance." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd564.pdf.
Повний текст джерелаGrant, Timothy John. "Inductive learning of knowledge-based planning operators." [Maastricht : Maastricht : Rijksuniversiteit Limburg] ; University Library, Maastricht University [Host], 1996. http://arno.unimaas.nl/show.cgi?fid=6686.
Повний текст джерелаGiulini, Ilaria. "Generalization bounds for random samples in Hilbert spaces." Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0026/document.
Повний текст джерелаThis thesis focuses on obtaining generalization bounds for random samples in reproducing kernel Hilbert spaces. The approach consists in first obtaining non-asymptotic dimension-free bounds in finite-dimensional spaces using some PAC-Bayesian inequalities related to Gaussian perturbations and then in generalizing the results in a separable Hilbert space. We first investigate the question of estimating the Gram operator by a robust estimator from an i. i. d. sample and we present uniform bounds that hold under weak moment assumptions. These results allow us to qualify principal component analysis independently of the dimension of the ambient space and to propose stable versions of it. In the last part of the thesis we present a new algorithm for spectral clustering. It consists in replacing the projection on the eigenvectors associated with the largest eigenvalues of the Laplacian matrix by a power of the normalized Laplacian. This iteration, justified by the analysis of clustering in terms of Markov chains, performs a smooth truncation. We prove nonasymptotic bounds for the convergence of our spectral clustering algorithm applied to a random sample of points in a Hilbert space that are deduced from the bounds for the Gram operator in a Hilbert space. Experiments are done in the context of image analysis
Truong, Hoang Vinh. "Multi color space LBP-based feature selection for texture classification." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0468/document.
Повний текст джерелаTexture analysis has been extensively studied and a wide variety of description approaches have been proposed. Among them, Local Binary Pattern (LBP) takes an essential part of most of color image analysis and pattern recognition applications. Usually, devices acquire images and code them in the RBG color space. However, there are many color spaces for texture classification, each one having specific properties. In order to avoid the difficulty of choosing a relevant space, the multi color space strategy allows using the properties of several spaces simultaneously. However, this strategy leads to increase the number of features extracted from LBP applied to color images. This work is focused on the dimensionality reduction of LBP-based feature selection methods. In this framework, we consider the LBP histogram and bin selection approaches for supervised texture classification. Extensive experiments are conducted on several benchmark color texture databases. They demonstrate that the proposed approaches can improve the state-of-the-art results
Sherif, Mohamed Ahmed Mohamed. "Automating Geospatial RDF Dataset Integration and Enrichment." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-215708.
Повний текст джерелаKostiadis, Kostas. "Learning to co-operate in multi-agent systems." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248696.
Повний текст джерелаKirke, Alexis John. "Learning and co-operation in mobile multi-robot systems." Thesis, University of Plymouth, 1997. http://hdl.handle.net/10026.1/1984.
Повний текст джерелаHammond, Alec Michael. "Machine Learning Methods for Nanophotonic Design, Simulation, and Operation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7131.
Повний текст джерелаHaque, Ashraful. "A Deep Learning-based Dynamic Demand Response Framework." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104927.
Повний текст джерелаDoctor of Philosophy
The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.
Bisen, Pradeep Siddhartha Singh. "Predicting Operator’s Choice During Airline Disruption Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18839.
Повний текст джерелаTurunen, Maria. "Learning an operatic role effectively : A case study of learning an operatic role in a time-saving way both vocally and on stage." Thesis, Stockholms konstnärliga högskola, Institutionen för opera, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uniarts:diva-886.
Повний текст джерелаHedman, Erik. "Data for Machine Learning : Data generation and simulation of a logistics operation for machine learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-67952.
Повний текст джерелаEdman, Anneli. "Combining Knowledge Systems and Hypermedia for User Co-operation and Learning." Doctoral thesis, Uppsala : Dept. of Information Science [Institutionen för informationsvetenskap], Univ, 2001. http://publications.uu.se/theses/91-506-1526-2/.
Повний текст джерелаHernández, Jiménez Enric. "Uncertainty and indistinguishability. Application to modelling with words." Doctoral thesis, Universitat Politècnica de Catalunya, 2007. http://hdl.handle.net/10803/6648.
Повний текст джерелаQuan totes les propietats són perfectament precises (absència d'incertesa), hom obtè la igualtat clàssica a on dos objectes són considerats iguals si i només si comparteixen el mateix conjunt de propietats. Però, què passa quan considerem l'aparició d'incertesa, com en el cas a on els objectes compleixen una determinada propietat només fins a un cert grau?. Llavors, donat que alguns objectes seran més similars entre si que d'altres, sorgeix la necessitat de una noció gradual del concepte d'igualtat.
Aquestes consideracions refermen la idea de que certs contextos requereixen una definició més flexible, que superi la rigidesa de la noció clàssica d'igualtat. Els operadors de T-indistingibilitat semblen bons candidats per aquest nou tipus d'igualtat que cerquem.
D'altra banda, La Teoria de l'Evidència de Dempster-Shafer, com a marc pel tractament d'evidències, defineix implícitament una noció d'indistingibilitat entre els elements del domini de discurs basada en la seva compatibilitat relativa amb l'evidència considerada. El capítol segon analitza diferents mètodes per definir l'operador de T-indistingibilitat associat a una evidència donada.
En el capítol tercer, després de presentar un exhaustiu estat de l'art en mesures d'incertesa, ens centrem en la qüestió del còmput de l'entropia quan sobre els elements del domini s'ha definit una relació d'indistingibilitat. Llavors, l'entropia hauria de ser mesurada no en funció de l'ocurrència d'events diferents, sinó d'acord amb la variabilitat percebuda per un observador equipat amb la relació d'indistingibilitat considerada. Aquesta interpretació suggereix el "paradigma de l'observador" que ens porta a la introducció del concepte d'entropia observacional.
La incertesa és un fenomen present al món real. El desenvolupament de tècniques que en permetin el tractament és doncs, una necessitat. La 'computació amb paraules' ('computing with words') pretén assolir aquest objectiu mitjançant un formalisme basat en etiquetes lingüístiques, en contrast amb els mètodes numèrics tradicionals. L'ús d'aquestes etiquetes millora la comprensibilitat del llenguatge de representació del
coneixement, a l'hora que requereix una adaptació de les tècniques inductives tradicionals.
En el quart capítol s'introdueix un nou tipus d'arbre de decisió que incorpora les indistingibilitats entre elements del domini a l'hora de calcular la impuresa dels nodes. Hem anomenat arbres de decisió observacionals a aquests nou tipus, donat que es basen en la incorporació de l'entropia observacional en la funció heurística de selecció d'atributs. A més, presentem un algorisme capaç d'induir regles lingüístiques mitjançant un tractament adient de la incertesa present a les etiquetes lingüístiques o a les dades mateixes. La definició de l'algorisme s'acompanya d'una comparació formal amb altres algorismes estàndards.
The concept of equality is a fundamental notion in any theory since it is essential to the ability of discerning the objects to whom it concerns, ability which in turn is a requirement for any classification mechanism that might be defined.
When all the properties involved are entirely precise, what we obtain is the classical equality, where two individuals are considered equal if and only if they share the same set of properties. What happens, however, when imprecision arises as in the case of properties which are fulfilled only up to a degree? Then, because certain individuals will be more similar than others, the need for a gradual notion of equality arises.
These considerations show that certain contexts that are pervaded with uncertainty require a more flexible concept of equality that goes beyond the rigidity of the classic concept of equality. T-indistinguishability operators seem to be good candidates for this more flexible and general version of the concept of equality that we are searching for.
On the other hand, Dempster-Shafer Theory of Evidence, as a framework for representing and managing general evidences, implicitly conveys the notion of indistinguishability between the elements of the domain of discourse based on their relative compatibility with the evidence at hand. In chapter two we are concerned with providing definitions for the T-indistinguishability operator associated to a given body of evidence.
In chapter three, after providing a comprehensive summary of the state of the art on measures of uncertainty, we tackle the problem of computing entropy when an indistinguishability relation has been defined over the elements of the domain. Entropy should then be measured not according to the occurrence of different events, but according to the variability perceived by an observer equipped with indistinguishability abilities as defined by the indistinguishability relation considered. This idea naturally leads to the introduction of the concept of observational entropy.
Real data is often pervaded with uncertainty so that devising techniques intended to induce knowledge in the presence of uncertainty seems entirely advisable.
The paradigm of computing with words follows this line in order to provide a computation formalism based on linguistic labels in contrast to traditional numerical-based methods.
The use of linguistic labels enriches the understandability of the representation language, although it also requires adapting the classical inductive learning procedures to cope with such labels.
In chapter four, a novel approach to building decision trees is introduced, addressing the case when uncertainty arises as a consequence of considering a more realistic setting in which decision maker's discernment abilities are taken into account when computing node's impurity measures. This novel paradigm results in what have been called --observational decision trees' since the main idea stems from the notion of observational entropy in order to incorporate indistinguishability concerns.
In addition, we present an algorithm intended to induce linguistic rules from data by properly managing the uncertainty present either in the set of describing labels or in the data itself. A formal comparison with standard algorithms is also provided.
Meyer, Ann Elizabeth. "Effects of experience and task inconsistency : a study of novice and expert cash register operators." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/29369.
Повний текст джерелаKaylani, Assem. "AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2538.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD
Lehmann, Jens. "Learning OWL Class Expressions." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-38351.
Повний текст джерелаJin, Lei. "The Impact of Co-operation Policies on Participation in Online Learning Object Exchange: A Preliminary Investigation." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/869.
Повний текст джерелаBenadict, Rajasegaram Annet. "The application of post-project reviews in events management by cultural operators." Thesis, Umeå universitet, Företagsekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-118291.
Повний текст джерелаSalim, Adil. "Random monotone operators and application to stochastic optimization." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT021/document.
Повний текст джерелаThis thesis mainly studies optimization algorithms. Programming problems arising in signal processing and machine learning are composite in many cases, i.e they exhibit constraints and non smooth regularization terms. Proximal methods are known to be efficient to solve such problems. However, in modern applications of data sciences, functions to be minimized are often represented as statistical expectations, whose evaluation is intractable. This cover the case of online learning, big data problems and distributed computation problems. To solve this problems, we study in this thesis proximal stochastic methods, that generalize proximal algorithms to the case of cost functions written as expectations. Stochastic proximal methods are first studied with a constant step size, using stochastic approximation techniques. More precisely, the Ordinary Differential Equation method is adapted to the case of differential inclusions. In order to study the asymptotic behavior of the algorithms, the stability of the sequences of iterates (seen as Markov chains) is studied. Then, generalizations of the stochastic proximal gradient algorithm with decreasing step sizes are designed to solve composite problems. Every quantities used to define the optimization problem are written as expectations. This include a primal dual algorithm to solve regularized and linearly constrained problems and an optimization over large graphs algorithm
Katzenbach, Michael. "Individual Approaches in Rich Learning Situations Material-based Learning with Pinboards." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-80328.
Повний текст джерелаTodd, Nicole Ann. "Support teachers, learning difficulties and secondary school culture." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/45779/1/Nicole_Todd_Thesis.pdf.
Повний текст джерелаFattic, Jana R. "Determining the Viability of a Hybrid Experiential and Distance Learning Educational Model for Water Treatment Plant Operators in Kentucky." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1082.
Повний текст джерелаTamaddoni, Nezhad Alireza. "Logic-based machine learning using a bounded hypothesis space : the lattice structure, refinement operators and a genetic algorithm approach." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/29849.
Повний текст джерелаJin, Lei. "The impact of co-operation policies on participation in online learning objective exchange a preliminary investigation /." Waterloo, Ont. : University of Waterloo, [Dept. of Management Sciences], 2002. http://etd.uwaterloo.ca/etd/ljin2002.pdf.
Повний текст джерела"A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Applied Science in Management Sciences". Includes bibliographical references.
Hassan, Mohamed Elhafiz. "Power Plant Operation Optimization : Unit Commitment of Combined Cycle Power Plants Using Machine Learning and MILP." Thesis, mohamed-ahmed@siemens.com, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395304.
Повний текст джерелаThaibah, Hilal. "Managing a Hybrid Oral Medication Distribution System in a Pediatric Hospital: A Machine Learning Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1626356839363113.
Повний текст джерелаLlofriu, Alonso Martin I. "Multi-Scale Spatial Cognition Models and Bio-Inspired Robot Navigation." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6888.
Повний текст джерелаHartley, Sally Ann. "Learning for development through co-operation : the engagement of youth with co-operatives in Lesotho and Uganda." Thesis, Open University, 2012. http://oro.open.ac.uk/54667/.
Повний текст джерелаTiclavilca, Andres M. "Multivariate Bayesian Machine Learning Regression for Operation and Management of Multiple Reservoir, Irrigation Canal, and River Systems." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/600.
Повний текст джерелаTucker, Mark Alan. "The influence of social-learning factors on farm operators' perceptions of agricultural-chemical risk in the Ohio Darby Creek hydrologic unit." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1239623499.
Повний текст джерелаTucker, Mark A. "The influence of social-learning factors on farm operators' perceptions of agricultural-chemical risk in the Ohio Darby Creek hydrologic unit /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487929230739381.
Повний текст джерелаHendrich, Christopher. "Proximal Splitting Methods in Nonsmooth Convex Optimization." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-149548.
Повний текст джерелаHogsholm, Robin Wagner. "Impact of a goal setting procedure on the work performance of young adults with behavioral/emotional/learning challenges." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000402.
Повний текст джерелаXiang, Yusheng, and Marcus Geimer. "Optimization of operation strategy for primary torque based hydrostatics drivetrain using artificial intelligence." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71073.
Повний текст джерелаBelkin, Mikhail. "Problems of learning on manifolds /." 2003. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3097083.
Повний текст джерела"Fast Graph Laplacian regularized kernel learning via semidefinite-quadratic-linear programming." 2011. http://library.cuhk.edu.hk/record=b5894621.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (p. 30-34).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Preliminaries --- p.4
Chapter 2.1 --- Kernel Learning Theory --- p.4
Chapter 2.1.1 --- Positive Semidefinite Kernel --- p.4
Chapter 2.1.2 --- The Reproducing Kernel Map --- p.6
Chapter 2.1.3 --- Kernel Tricks --- p.7
Chapter 2.2 --- Spectral Graph Theory --- p.8
Chapter 2.2.1 --- Graph Laplacian --- p.8
Chapter 2.2.2 --- Eigenvectors of Graph Laplacian --- p.9
Chapter 2.3 --- Convex Optimization --- p.10
Chapter 2.3.1 --- From Linear to Conic Programming --- p.11
Chapter 2.3.2 --- Second-Order Cone Programming --- p.12
Chapter 2.3.3 --- Semidefinite Programming --- p.12
Chapter 3 --- Fast Graph Laplacian Regularized Kernel Learning --- p.14
Chapter 3.1 --- The Problems --- p.14
Chapter 3.1.1 --- MVU --- p.16
Chapter 3.1.2 --- PCP --- p.17
Chapter 3.1.3 --- Low-Rank Approximation: from SDP to QSDP --- p.18
Chapter 3.2 --- Previous Approach: from QSDP to SDP --- p.20
Chapter 3.3 --- Our Formulation: from QSDP to SQLP --- p.21
Chapter 3.4 --- Experimental Results --- p.23
Chapter 3.4.1 --- The Results --- p.25
Chapter 4 --- Conclusion --- p.28
Bibliography --- p.30