Dissertations / Theses on the topic 'Online learning algorithms'
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Harrington, Edward Francis. "Aspects of online learning /." View thesis entry in Australian Digital Theses Program, 2004. http://thesis.anu.edu.au/public/adt-ANU20060328.160810/index.html.
Full textHarrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.
Full textPasteris, S. U. "Efficient algorithms for online learning over graphs." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.
Full textPacker, Heather S. "Evolving ontologies with online learning and forgetting algorithms." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/194923/.
Full textLi, Le. "Online stochastic algorithms." Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0031.
Full textThis thesis works mainly on three subjects. The first one is online clustering in which we introduce a new and adaptive stochastic algorithm to cluster online dataset. It relies on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that this algorithm has a regret bound of the order of and is asymptotically minimax under the constraint on the number of clusters. A RJMCMC-flavored implementation is also proposed. The second subject is related to the sequential learning of principal curves which seeks to represent a sequence of data by a continuous polygonal curve. To this aim, we introduce a procedure based on the MAP of Gibbs-posterior that can give polygonal lines whose number of segments can be chosen automatically. We also show that our procedure is supported by regret bounds with sublinear remainder terms. In addition, a greedy local search implementation that incorporates both sleeping experts and multi-armed bandit ingredients is presented. The third one concerns about the work which aims to fulfilling practical tasks within iAdvize, the company which supports this thesis. It includes sentiment analysis for textual messages by using methods in both text mining and statistics, and implementation of chatbot based on nature language processing and neural networks
Minerva, Michela. "Automated Configuration of Offline/Online Algorithms: an Empirical Model Learning Approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22649/.
Full textPesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38190.
Full textAl-Janabi, Mohammed Fadhil Zamil. "Detection of suspicious URLs in online social networks using supervised machine learning algorithms." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5581/.
Full textZheng, Zhilin. "Learning Group Composition and Re-composition in Large-scale Online Learning Contexts." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18412.
Full textSmall learning group composition addresses the problem of seeking such matching among a population of students that it could bring each group optimal benefits. Recently, many studies have been conducted to address this small group composition problem. Nevertheless, the focus of such a body of research has rarely been cast to large-scale contexts. Due to the recent come of MOOCs, the topic of group composition needs to be accordingly extended with new investigations in such large learning contexts. Different from classroom settings, the reported high drop-out rate of MOOCs could result in group’s incompletion in size and thus might compel many students to compose new groups. Thus, in addition to group composition, group re-composition as a new topic needs to be studied in current large-scale learning contexts as well. In this thesis, the research is structured in two stages. The first stage is group composition. In this part, I proposed a discrete-PSO algorithm to compose small learning groups and compared the existing group composition algorithms from the perspectives of time cost and grouping quality. To implement group composition in MOOCs, a group composition experiment was conducted in a MOOC. The main results indicate that group composition can reduce drop-out rate, yet has a very weak association with students’ learning performance. The second stage is to cope with group re-composition. This thesis suggests a data-driven approach that makes full use of group interaction data and accounts for group dynamics. Through evaluation in a simulation experiment, it shows its advantages of bringing us more cohesive learning groups and reducing the drop-out rate compared to a random condition. Apart from these, a group learning tool that fulfills the goals of the proposed group re-composition approach has been developed and is made ready for practice.
Heidari, Fariba. "Quality of service routing using decentralized learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115672.
Full textWe investigate the performance degradation due to decentralized routing as opposed to centralized optimal routing policies in practical scenarios. The system optimal and the Nash bargaining solutions are two centralized benchmarks used in this study. We provide nonlinear programming formulations of these problems along with a distributed recursive approach to compute the solutions. An on-line partially-decentralized control architecture is also proposed to achieve the system optimal and the Nash bargaining solution performances. Numerical results in some practical scenarios with well engineered networks, where the network resources and traffic demand are well matched, indicate that decentralized learning techniques provide efficient, stable and scalable approaches for routing the bandwidth guaranteed paths.
In the context of on-line learning, we propose a new algorithm to track the best action-selection policy when it abruptly changes over time. The proposed algorithm employs change detection mechanisms to detect the sudden changes and restarts the learning process on the detection of an abrupt change. The performance analysis of this study reveals that when all the changes are detectable by the change detection mechanism, the proposed tracking the best action-selection policy algorithm is rate optimal. On-line routing of bandwidth guaranteed paths with the potential occurrence of network shocks such as significant changes in the traffic demand is one of the applications of the devised algorithm. Simulation results show the merit of the proposed algorithm in tracking the optimal routing policy when it abruptly changes.
Melki, Gabriella A. "Fast Online Training of L1 Support Vector Machines." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4282.
Full textHolmgren, Faghihi Josef, and Paul Gorgis. "Time efficiency and mistake rates for online learning algorithms : A comparison between Online Gradient Descent and Second Order Perceptron algorithm and their performance on two different data sets." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260087.
Full textDen här avhandlingen undersöker skillnaden mellan två olika “online learning”-algoritmer: Online Gradient Descent och Second-Order Perceptron, och hur de presterar på olika datasets med fokus på andelen felklassificeringar, tidseffektivitet och antalet uppdateringar. Genom att studera olika “online learning”-algoritmer och hur de fungerar i olika miljöer, kommer det hjälpa till att förstå och utveckla nya strategier för att hantera vidare “online learning”-problem. Studien inkluderar två olika dataset, Pima Indians Diabetes och Mushroom, och använder biblioteket LIBOL för testning. Resultatet i denna avhandling visar att Online Gradient Descent presterar bättre överlag på de testade dataseten. För det första datasetet visade Online Gradient Descent ett betydligt lägre andel felklassificeringar. För det andra datasetet visade OGD lite högre andel felklassificeringar, men samtidigt var algoritmen anmärkningsvärt mer tidseffektiv i jämförelse med Second-Order Perceptron. Framtida studier inkluderar en bredare testning med mer, och olika, datasets och andra relaterade algoritmer. Det leder till bättre resultat och höjer trovärdigheten.
Dennis, Aaron W. "Algorithms for Learning the Structure of Monotone and Nonmonotone Sum-Product Networks." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6188.
Full textCrocomo, Márcio Kassouf. "Um algoritmo evolutivo para aprendizado on-line em jogos eletrônicos." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-09052008-160236/.
Full textThe goal of this work is to verify if it is possible to apply Evolutionary Algorithms to online learning in computer games. Some authors agree that evolutionary algorithms do not work properly in that case. With the objective of contesting this affirmation, this work was performed. To accomplish the goal of this work, a computer game was developed, in which the learning algorithm must create intelligent and adaptive strategies to control the non-player characters using an evolutionary algorithm. Therefore, the aim of the evolutionary algorithm is to adapt the strategy used by the computer according to the player\'s actions during the game. A review on Evolutionary Computation and the techniques used to produce intelligent behaviors for the computer controlled characters in modern game is presented, exposing the advantages, the problems and some applications of each technique. The proposed game is also explained, together with the implemented algorithms, the experiments and the obtained results. Finally, it is presented a comparison between the implemented algorithm and the Dynamic Script technique. Thus, this work offers contributions to the fields of Evolutionary Computation and Artificial Intelligence applied to games
Al, Rawashdeh Khaled. "Toward a Hardware-assisted Online Intrusion Detection System Based on Deep Learning Algorithms for Resource-Limited Embedded Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535464571843315.
Full textRekanar, Kaavya. "Text Classification of Legitimate and Rogue online Privacy Policies : Manual Analysis and a Machine Learning Experimental Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13363.
Full textFrery, Jordan. "Ensemble Learning for Extremely Imbalced Data Flows." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES034.
Full textMachine learning is the study of designing algorithms that learn from trainingdata to achieve a specific task. The resulting model is then used to predict overnew (unseen) data points without any outside help. This data can be of manyforms such as images (matrix of pixels), signals (sounds,...), transactions (age,amount, merchant,...), logs (time, alerts, ...). Datasets may be defined to addressa specific task such as object recognition, voice identification, anomaly detection,etc. In these tasks, the knowledge of the expected outputs encourages a supervisedlearning approach where every single observed data is assigned to a label thatdefines what the model predictions should be. For example, in object recognition,an image could be associated with the label "car" which suggests that the learningalgorithm has to learn that a car is contained in this picture, somewhere. This is incontrast with unsupervised learning where the task at hand does not have explicitlabels. For example, one popular topic in unsupervised learning is to discoverunderlying structures contained in visual data (images) such as geometric formsof objects, lines, depth, before learning a specific task. This kind of learning isobviously much harder as there might be potentially an infinite number of conceptsto grasp in the data. In this thesis, we focus on a specific scenario of thesupervised learning setting: 1) the label of interest is under represented (e.g.anomalies) and 2) the dataset increases with time as we receive data from real-lifeevents (e.g. credit card transactions). In fact, these settings are very common inthe industrial domain in which this thesis takes place
Berthold, Oswald. "Robotic self-exploration and acquisition of sensorimotor skills." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21480.
Full textThe interaction of machines with their environment should be reliable, safe, and ecologically adequate. To ensure this over long-term complex scenarios, a theory of adaptive behavior is needed. In developmental robotics, and embodied artificial intelligence behavior is regarded as a phenomenon that emerges from an ongoing dynamic interaction between entities called agent, body, and environment. The thesis investigates robots that are able to learn rapidly and on their own, how to do primitive motions, using sensorimotor information. The long-term goal is to reuse acquired skills when learning other motions in the future, and thereby grow a complex repertoire of possible interactions with the world, that is fully grounded in, and continually adapted to sensorimotor experience through developmental processes. Using methods from machine learning, neuroscience, statistics, and physics, the question is decomposed into the relationship of representation, exploration, and learning. A framework is provided for systematic variation and evaluation of models. The proposed framework considers procedural generation of hypotheses as scientific workflows using a fixed set of functional building blocks, and allows to search for models by seamless evaluation in simulation and real world experiments. Additional contributions of the thesis are related to the agent's causal footprint in sensorimotor time. A probabilistic graphical model is provided, along with an information-theoretic learning algorithm, to discover networks of information flow in sensorimotor data. A generic developmental model, based on real time prediction learning, is presented and discussed on the basis of three different algorithmic variations.
Elahi, Haroon. "A Boosted-Window Ensemble." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5658.
Full textMurphy, Nicholas John. "An online learning algorithm for technical trading." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31048.
Full textLaflamme, Simon M. Eng Massachusetts Institute of Technology. "Online learning algorithm for structural control using magnetorheological actuators." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39271.
Full textIncludes bibliographical references (p. 83-84).
Magnetorheological actuators are promising devices for mitigating vibrations because they only require a fraction of energy for a similar performance to active control. Conversely, these semi-active devices have limited maximum forces and are hard to model due to the rheological properties of their fluid. When considering structural control, classical theories necessitate full knowledge of the structural dynamic states and properties most of which can only be estimated when considering large-scale control, which may be difficult or inaccurate for complicated geometries due to the non-linear behaviour of structures. Additionally, most of these theories do not take into account the response delay of the actuators which may result in structural instabilities. To address the problem, learning algorithms using offline learning have been proposed in order to have the structure learn its behaviour, but they can be perceived as unrealistic because earthquake data can hardly be produced to train these schemes. Here, an algorithm using online learning feedback is proposed to address this problem where the structure observes, compares and adapts its performance at each time step, analogous to a child learning his or her motor functions.
(cont.) The algorithm uses a machine learning technique, Gaussian kernels, to prescribe forces upon structural states, where states are evaluated strictly based on displacement and acceleration feedback. The algorithm has been simulated and performances assessed by comparing it with two classical control theories: clipped-optimal and passive controls. The proposed scheme is found to be stable and performs well in mitigating vibrations for a low energy input, but does not perform as well compared to clipped-optimal case. This relative performance would be expected to be better for large-scale structures because of the adaptability of the proposed algorithm.
by Simon Laflamme.
M.Eng.
Brégère, Margaux. "Stochastic bandit algorithms for demand side management Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders Online Hierarchical Forecasting for Power Consumption Data Target Tracking for Contextual Bandits : Application to Demand Side Management." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM022.
Full textAs electricity is hard to store, the balance between production and consumption must be strictly maintained. With the integration of intermittent renewable energies into the production mix, the management of the balance becomes complex. At the same time, the deployment of smart meters suggests demand response. More precisely, sending signals - such as changes in the price of electricity - would encourage users to modulate their consumption according to the production of electricity. The algorithms used to choose these signals have to learn consumer reactions and, in the same time, to optimize them (exploration-exploration trade-off). Our approach is based on bandit theory and formalizes this sequential learning problem. We propose a first algorithm to control the electrical demand of a homogeneous population of consumers and offer T⅔ upper bound on its regret. Experiments on a real data set in which price incentives were offered illustrate these theoretical results. As a “full information” dataset is required to test bandit algorithms, a consumption data generator based on variational autoencoders is built. In order to drop the assumption of the population homogeneity, we propose an approach to cluster households according to their consumption profile. These different works are finally combined to propose and test a bandit algorithm for personalized demand side management
Peel, Thomas. "Algorithmes de poursuite stochastiques et inégalités de concentration empiriques pour l'apprentissage statistique." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4769/document.
Full textThe first part of this thesis introduces new algorithms for the sparse encoding of signals. Based on Matching Pursuit (MP) they focus on the following problem : how to reduce the computation time of the selection step of MP. As an answer, we sub-sample the dictionary in line and column at each iteration. We show that this theoretically grounded approach has good empirical performances. We then propose a bloc coordinate gradient descent algorithm for feature selection problems in the multiclass classification setting. Thanks to the use of error-correcting output codes, this task can be seen as a simultaneous sparse encoding of signals problem. The second part exposes new empirical Bernstein inequalities. Firstly, they concern the theory of the U-Statistics and are applied in order to design generalization bounds for ranking algorithms. These bounds take advantage of a variance estimator and we propose an efficient algorithm to compute it. Then, we present an empirical version of the Bernstein type inequality for martingales by Freedman [1975]. Again, the strength of our result lies in the variance estimator computable from the data. This allows us to propose generalization bounds for online learning algorithms which improve the state of the art and pave the way to a new family of learning algorithms taking advantage of this empirical information
Provatas, Spyridon. "An Online Machine Learning Algorithm for Heat Load Forecasting in District Heating Systems." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3475.
Full textBarbosa, Haline Pereira de Oliveira, and 5592991791259. "Detecção de Phishing no Twitter Baseada em Algoritmos de Aprendizagem Online." Universidade Federal do Amazonas, 2018. https://tede.ufam.edu.br/handle/tede/6778.
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Twitter is one of the most used social networks in the world with about 328 million users sharing images, videos, texts and links. Due to the restrictions on message size it is common for tweets to share shortened links to websites, making it impossible to visually identify the URL before knowing what will be displayed. Faced with this scenario, Twitter becomes a means of spreading phishing attacks through malicious links. Phishing is an attack that seeks to obtain personal information like name, CPF, passwords, number of bank accounts and numbers of credit cards. Twitter phishing attack detection systems are usually built using off-line supervised machine learning, where a large amount of data is examined once to induce a single static prediction model. In these systems, the incorporation of new data requires the reconstruction of the prediction model from the processing of the entire database, making this process slow and inefficient. In this work we propose a framework to detect phishing in Twitter. The framework uses supervised online learning, that is, the classifier is updated with each processed tweet and, if it makes a wrong prediction, the model is updated by adapting quickly to the changes with low computational cost, time and maintaining its efficiency in the task of ranking. For this study we evaluated the performance of the online learning algorithms Adaptive Random Forest, Hoeffding Tree, Naive Bayes, Perceptron and Stochastic Gradient Descent. The online Adaptive Random Forest classifier presented 99.8% prequential accuracy in the classification of phishing tweets.
O Twitter é uma das redes sociais mais utilizadas no mundo com cerca de centenas de milhões de usuários compartilhando imagens, vídeos, textos e links. Devido às restrições impostas no tamanho das mensagens é comum que os tweets compartilhem links encurtados para websites impossibilitando a identificação visual prévia da URL antes de saber o que será exibido. Tal problema tornou o Twitter um dos principais meios de disseminação de ataques de phishing através de links maliciosos. Phishing é um ataque que visa obter informações pessoais como nomes, senhas, números de contas bancárias e de cartões de crédito. Em geral, os sistemas de detecção de ataques de phishing projetados para o Twitter são construídos com base em modelos de classificação off-line. Em tais sistemas, um grande volume de dados é examinado uma única vez para induzir em um único modelo de predição estático. Nesses sistemas, a incorporação de novos dados requer a reconstrução do modelo de previsão a partir do processamento de toda a base de dados, tornando esse processo lento e ineficiente. Para solucionar este problema, este trabalho propõe um framework de detecção de phishing no Twitter. O framework utiliza aprendizagem online supervisionada, ou seja, o classificador é atualizado a cada tweet processado e, caso este realize uma predição errada, o modelo é atualizado se adaptando rapidamente às mudanças com baixo custo computacional, tempo e mantendo a sua eficiência na tarefa de classificação. Para este estudo avaliamos o desempenho dos algoritmos de aprendizagem online Adaptive Random Forest, Hoeffding Tree, Naive Bayes, Perceptron e Stochastic Gradient Descent. O classificador online Adaptive Random Forest apresentou acurácia prequential 99,8%, na classificação de tweets de phishing.
Farghally, Mohammed Fawzi Seddik. "Visualizing Algorithm Analysis Topics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73539.
Full textPh. D.
Liakopoulos, Nikolaos. "Machine Learning Techniques for Online Resource Allocation in Wireless Networks." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS529.
Full textTraditionally, network optimization is used to provide good configurations in real network system problems based on mathematical models and statistical assumptions. Recently, this paradigm is evolving, fueled by an explosion of availability of data. The modern trend in networking problems is to tap into the power of data to extract models and deal with uncertainty. This thesis proposes algorithmic frameworks for wireless networks, based both on classical or data-driven optimization and machine learning. We target two use cases, user association and cloud resource reservation.The baseline approach for user association, connecting wireless devices to the base station that provides the strongest signal, leads to very inefficient configurations even in current wireless networks. We focus on tailoring user association based on resource efficiency and service requirement satisfaction, depending on the underlying network demand. We first study distributed user association with priority QoS guarantees, then scalable centralized load balancing based on computational optimal transport and finally robust user association based on approximate traffic prediction.Moving to the topic of cloud resource reservation, we develop a novel framework for resource reservation in worst-case scenaria, where the demand is engineered by an adversary aiming to harm our performance. We provide policies that have ``no regret'' and guarantee asymptotic feasibility in budget constraints under such workloads. More importantly we expand to a general framework for online convex optimization (OCO) problems with long term budget constraints complementing the results of recent literature in OCO
Holm, Raven R. "Natural language processing of online propaganda as a means of passively monitoring an adversarial ideology." Thesis, Monterey, California: Naval Postgraduate School, 2017. http://hdl.handle.net/10945/52993.
Full textReissued 30 May 2017 with Second Reader’s non-NPS affiliation added to title page.
Online propaganda embodies a potent new form of warfare; one that extends the strategic reach of our adversaries and overwhelms analysts. Foreign organizations have effectively leveraged an online presence to influence elections and distance-recruit. The Islamic State has also shown proficiency in outsourcing violence, proving that propaganda can enable an organization to wage physical war at very little cost and without the resources traditionally required. To augment new counter foreign propaganda initiatives, this thesis presents a pipeline for defining, detecting and monitoring ideology in text. A corpus of 3,049 modern online texts was assembled and two classifiers were created: one for detecting authorship and another for detecting ideology. The classifiers demonstrated 92.70% recall and 95.84% precision in detecting authorship, and detected ideological content with 76.53% recall and 95.61% precision. Both classifiers were combined to simulate how an ideology can be detected and how its composition could be passively monitored across time. Implementation of such a system could conserve manpower in the intelligence community and add a new dimension to analysis. Although this pipeline makes presumptions about the quality and integrity of input, it is a novel contribution to the fields of Natural Language Processing and Information Warfare.
Lieutenant, United States Coast Guard
Duarte, Kevin. "Aide à la décision médicale et télémédecine dans le suivi de l’insuffisance cardiaque." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0283/document.
Full textThis thesis is part of the "Handle your heart" project aimed at developing a drug prescription assistance device for heart failure patients. In a first part, a study was conducted to highlight the prognostic value of an estimation of plasma volume or its variations for predicting major short-term cardiovascular events. Two classification rules were used, logistic regression and linear discriminant analysis, each preceded by a stepwise variable selection. Three indices to measure the improvement in discrimination ability by adding the biomarker of interest were used. In a second part, in order to identify patients at short-term risk of dying or being hospitalized for progression of heart failure, a short-term event risk score was constructed by an ensemble method, two classification rules, logistic regression and linear discriminant analysis of mixed data, bootstrap samples, and by randomly selecting predictors. We define an event risk measure by an odds-ratio and a measure of the importance of variables and groups of variables using standardized coefficients. We show a property of linear discriminant analysis of mixed data. This methodology for constructing a risk score can be implemented as part of online learning, using stochastic gradient algorithms to update online the predictors. We address the problem of sequential multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process. To avoid the phenomenon of numerical explosion which can be encountered and to reduce the computing time in order to take into account a maximum of arriving data, we propose to use a process with online standardized data instead of raw data and to use of several observations per step or all observations until the current step. We define three processes and study their almost sure convergence, one with a variable step-size, an averaged process with a constant step-size, a process with a constant or variable step-size and the use of all observations until the current step without storing them. These processes are compared to classical processes on 11 datasets. The third defined process with constant step-size typically yields the best results
Raykhel, Ilya Igorevitch. "Real-Time Automatic Price Prediction for eBay Online Trading." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1631.
Full textLabernia, Fabien. "Algorithmes efficaces pour l’apprentissage de réseaux de préférences conditionnelles à partir de données bruitées." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED018/document.
Full textThe rapid growth of personal web data has motivated the emergence of learning algorithms well suited to capture users’ preferences. Among preference representation formalisms, conditional preference networks (CP-nets) have proven to be effective due to their compact and explainable structure. However, their learning is difficult due to their combinatorial nature.In this thesis, we tackle the problem of learning CP-nets from corrupted large datasets. Three new algorithms are introduced and studied on both synthetic and real datasets.The first algorithm is based on query learning and considers the contradictions between multiple users’ preferences by searching in a principled way the variables that affect the preferences. The second algorithm relies on information-theoretic measures defined over the induced preference rules, which allow us to deal with corrupted data. An online version of this algorithm is also provided, by exploiting the McDiarmid's bound to define an asymptotically optimal decision criterion for selecting the best conditioned variable and hence allowing to deal with possibly infinite data streams
Cayuela, Rafols Marc. "Algorithmic Study on Prediction with Expert Advice : Study of 3 novel paradigms with Grouped Experts." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254344.
Full textHuvudarbetet för den här avhandlingen har varit en grundlig studie av den nya Prediction with Partially Monitored Grouped Expert Advice and Side Information paradigmet. Detta är nyligen föreslagit i denna avhandling, och det utökar det brett studerade Prediction with Expert Advice paradigmet. Förlängningen baseras på två antaganden och en begränsning som ändrar det ursprungliga problemet. Det första antagandet, Grouped, förutsätter att experterna är inbyggda i grupper. Det andra antagandet, Side Information, introducerar ytterligare information som kan användas för att i tid relatera förutsägelser med grupper. Slutligen innebär begränsningen, Partially Monitored, att gruppens förutsägelser endast är kända för en grupp i taget. Studien av detta paradigm innefattar utformningen av en komplett förutsägelsesalgoritm, beviset på en teoretisk bindning till det sämre fallet kumulativa ånger för en sådan algoritm och en experimentell utvärdering av algoritmen (bevisar förekomsten av fall där detta paradigm överträffar Prediction with Expert Advice). Eftersom algoritmens utveckling är konstruktiv tillåter den dessutom att enkelt bygga två ytterligare prediksionsalgoritmer för Prediction with Grouped Expert Advice och Prediction with Grouped Expert Advice and Side Information paradigmer. Därför presenterar denna avhandling tre nya prediktionsalgoritmer med motsvarande ångergränser och en jämförande experimentell utvärdering inklusive det ursprungliga Prediction with Expert Advice paradigmet.
SILVA, Márcio Eduardo Gonçalves. "Algoritmos da Família LMS para a Solução Aproximada da HJB em Projetos Online de Controle Ótimo Discreto Multivariável e Aprendizado por Reforço." Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/1891.
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The technique of linear control based on the minimization of a quadratic performance index using the second method of Lyapunov to guarantee the stability of the system, if this is controllable and observable. however, this technique is inevitably necessary to find the solution of the HJB or Riccati equation. The control system design online need, real time, to adjust your feedback gain to maintain a certain dynamic, it requires the calculation of the Riccati equation solution in each sampling generating a large computational load that can derail its implementation. This work shows an intelligent control system design that meets the optimal or suboptimal control action from the sensory data of process states and the instantaneous cost observed after each state transition. To find this optimal control action or policy, the approximate dynamic programming and adaptive critics are used, based on the parameterizations given by the problem of linear quadratic regulator (LQR), but without explicitly solving the associated Riccati equation. More specifically, the LQR problem is solved by four different methods which are the Dynamic Programming Heuristic, the Dual Heuristic Dynamic Programming, Action Dependent Dynamic Programming Heuristic and Action Dependent Dual Heuristic Dynamic Programming algorithms. However, these algorithms depend on knowledge of the value functions to derive the optimal control actions. These value functions with known structures have their parameters estimated using the least mean square family and Recursive Least Squares algorithms. Two processes that have the Markov property were used in the computational validation of the algorithms adaptive critics implemented, one corresponds to the longitudinal dynamics of an aircraft and the other to an electrical circuit.
A técnica de controle linear baseado na minimização de um índices de desempenho quadrático utilizando o segundo método de Liapunov garante a estabilidade do sistema, se este for controlável e observável. Por outro lado, nessa técnica inexoravelmente é necessário encontrar a solução da Equação Hamilton-Jacobi-Bellman (HJB) ou Riccati. Em projeto de sistema de controle online que necessita, em tempo real, alterar seus ganhos de retroação para manter uma certa dinâmica, impõe o cálculo da solução da equação de Riccati em cada instante de amostragem gerando uma grande carga computacional que pode inviabilizar sua implementação. Neste trabalho, mostra-se o projeto de um sistema de controle inteligente que encontra a ação de controle ótima ou subótima a partir de dados sensoriais dos estados do processo e do custo instantâneo observados após cada transição de estado. Para encontrar essa ação de controle ou política ótima, a programação dinâmica aproximada ou críticos adaptativos são utilizados, tendo como base as parametrizações dado pelo problema do regulador linear quadrático (LQR), mas sem resolver explicitamente a equação de Riccati associada. Mais especificamente, o problema do LQR é resolvido por quatro métodos distintos que são os algoritmos de Programação Dinâmica Heurística, a Programação Dinâmica Heurística Dual, a Programação Dinâmica Heurística Dependente de Ação e a Programação Dinâmica Heurística Dual Dependente de Ação. Entretanto, esses algoritmos dependem do conhecimento das funções valor para, assim, derivar as ações de controle ótimas. Essas funções valor com estruturas conhecidas tem seus parâmetros estimados utilizando os algoritmos da família dos mínimos quadrados médios e o algoritmo de Mínimos Quadrados Recursivo. Dois processos que obedecem à propriedade de Markov foram empregados na validação computacional dos algoritmos críticos adaptativos, um corresponde à dinâmica longitudinal de uma aeronave e o outro à de um circuito elétrico.
Moscu, Mircea. "Inférence distribuée de topologie de graphe à partir de flots de données." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4081.
Full textThe second decade of the current millennium can be summarized in one short phrase: the advent of data. There has been a surge in the number of data sources: from audio-video streaming, social networks and the Internet of Things, to smartwatches, industrial equipment and personal vehicles, just to name a few. More often than not, these sources form networks in order to exchange information. As a direct consequence, the field of Graph Signal Processing has been thriving and evolving. Its aim: process and make sense of all the surrounding data deluge.In this context, the main goal of this thesis is developing methods and algorithms capable of using data streams, in a distributed fashion, in order to infer the underlying networks that link these streams. Then, these estimated network topologies can be used with tools developed for Graph Signal Processing in order to process and analyze data supported by graphs. After a brief introduction followed by motivating examples, we first develop and propose an online, distributed and adaptive algorithm for graph topology inference for data streams which are linearly dependent. An analysis of the method ensues, in order to establish relations between performance and the input parameters of the algorithm. We then run a set of experiments in order to validate the analysis, as well as compare its performance with that of another proposed method of the literature.The next contribution is in the shape of an algorithm endowed with the same online, distributed and adaptive capacities, but adapted to inferring links between data that interact non-linearly. As such, we propose a simple yet effective additive model which makes use of the reproducing kernel machinery in order to model said nonlinearities. The results if its analysis are convincing, while experiments ran on biomedical data yield estimated networks which exhibit behavior predicted by medical literature.Finally, a third algorithm proposition is made, which aims to improve the nonlinear model by allowing it to escape the constraints induced by additivity. As such, the newly proposed model is as general as possible, and makes use of a natural and intuitive manner of imposing link sparsity, based on the concept of partial derivatives. We analyze this proposed algorithm as well, in order to establish stability conditions and relations between its parameters and its performance. A set of experiments are ran, showcasing how the general model is able to better capture nonlinear links in the data, while the estimated networks behave coherently with previous estimates
RÊGO, Patrícia Helena Moraes. "Aprendizagem por Reforço e Programação Dinâmica Aproximada para Controle Ótimo: Uma Abordagem para o Projeto Online do Regulador Linear Quadrático Discreto com Programação Dinâmica Heurística Dependente de Estado e Ação." Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/1879.
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In this thesis a proposal of an uni ed approach of dynamic programming, reinforcement learning and function approximation theories aiming at the development of methods and algorithms for design of optimal control systems is presented. This approach is presented in the approximate dynamic programming context that allows approximating the optimal feedback solution as to reduce the computational complexity associated to the conventional dynamic programming methods for optimal control of multivariable systems. Speci cally, in the state and action dependent heuristic dynamic programming framework, this proposal is oriented for the development of online approximated solutions, numerically stable, of the Riccati-type Hamilton-Jacobi-Bellman equation associated to the discrete linear quadratic regulator problem which is based on a formulation that combines value function estimates by means of a RLS (Recursive Least-Squares) structure, temporal di erences and policy improvements. The development of the proposed methodologies, in this work, is focused mainly on the UDU T factorization that is inserted in this framework to improve the RLS estimation process of optimal decision policies of the discrete linear quadratic regulator, by circumventing convergence and numerical stability problems related to the covariance matrix ill-conditioning of the RLS approach.
Apresenta-se nesta tese uma proposta de uma abordagem uni cada de teorias de programação dinâmica, aprendizagem por reforço e aproximação de função que tem por objetivo o desenvolvimento de métodos e algoritmos para projeto online de sistemas de controle ótimo. Esta abordagem é apresentada no contexto de programação dinâmica aproximada que permite aproximar a solução de realimentação ótima de modo a reduzir a complexidade computacional associada com métodos convencionais de programação dinâmica para controle ótimo de sistemas multivariáveis. Especi camente, no quadro de programação dinâmica heurística e programação dinâmica heurística dependente de ação, esta proposta é orientada para o desenvolvimento de soluções aproximadas online, numericamente estáveis, da equação de Hamilton-Jacobi-Bellman do tipo Riccati associada ao problema do regulador linear quadrático discreto que tem por base uma formulação que combina estimativas da função valor por meio de uma estrutura RLS (do inglês Recursive Least-Squares), diferenças temporais e melhorias de política. O desenvolvimento das metodologias propostas, neste trabalho, tem seu foco principal voltado para a fatoração UDU T que é inserida neste quadro para melhorar o processo de estimação RLS de políticas de decisão ótimas do regulador linear quadrá- tico discreto, contornando-se problemas de convergência e estabilidade numérica relacionados com o mal condicionamento da matriz de covariância da abordagem RLS.
Bubeck, Sébastien. "JEUX DE BANDITS ET FONDATIONS DU CLUSTERING." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2010. http://tel.archives-ouvertes.fr/tel-00845565.
Full textHarrington, Edward. "Aspects of Online Learning." Phd thesis, 2004. http://hdl.handle.net/1885/47147.
Full textRobards, Matthew Walters. "Online learning algorithms for reinforcement learning with function approximation." Phd thesis, 2011. http://hdl.handle.net/1885/150825.
Full textLakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization." Thesis, 2012. http://hdl.handle.net/2005/3245.
Full text賴翔偉. "Online Learning Algorithms based on Bayesian IRT models." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9gn772.
Full text國立政治大學
統計學系
106
In this paper, we present two types of online learning algorithms--statical and dynamical--to capture users’ and items’ latent traits’ information through online product rating data in a real-time manner. The statical one extends Weng and Coad (2018)’s deterministic moment-matching method by adding priors to cutpoints, and the dynamical one extends the statical one with the dynamical ideas adopted in Graepel et al. (2010) for taking users’ and items’ time-dependent latent traits into account. Both learning algorithms are designed for the Bayesian ordinal IRT model proposed by Ho and Quinn (2008). Through experiments, we have verified two things: First, updating cutpoints sequentially produces better results. Second, statical learning’s computational time is almost twice as less as dynamical learning’s, but dynamical learning can slightly outperform statical learning under some configurations. At the end of the paper, we give some useful configurations for setting up the priors of the latent variables of Ho and Quinn’s ordinal IRT model.
Chiu, Chien-Jung, and 邱健榮. "Design of an intelligent control system with online learning algorithms." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/23355470340408394886.
Full text國立中央大學
電機工程研究所
100
The traditional control system designs are always based on the system dynamic equations; however, it is difficult to be described as the plants are too complex. This dissertation proposes several intelligent control systems based on the adaptive control, sliding-mode control and neural network control technologies. For the adaptive proportional integral derivative (PID) controller design, the adaptive PID controller can automatically tune the controller gain factors based on the gradient descent method. For the adaptive neural network controllers design, a recurrent-wavelet-neural-network-based adaptive control, RBF-neural-network-based adaptive control and fuzzy-wavelet-neural-network-based adaptive control methods are proposed. In these control system designs, an online parameter tuning methodology, using the gradient descent method or the Lyapunov stability theorem, is developed to increase the learning capability and to guarantee the system’s stability. Moreover, a PID type adaptation tuning mechanism is derived to speed up the convergence of the tracking error and controller parameters. Furthermore, the dynamic-sliding-mode-neural-network-based adaptive control design method is developed with dynamic learning rate which is proposed to reduce the chattering phenomenon. Finally, the developed control system design methods are applied to some control system applications, such as induction servomotor system, brushless DC motor system, chaotic system and chaotic synchronization system. The simulation and experimental results have demonstrated that the effectiveness of the proposed design methods.
"Learning with Attributed Networks: Algorithms and Applications." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.54837.
Full textDissertation/Thesis
Doctoral Dissertation Computer Science 2019
Hendricks, Dieter. "An online adaptive learning algorithm for optimal trade execution in high-frequency markets." Thesis, 2016. http://hdl.handle.net/10539/21710.
Full textAutomated algorithmic trade execution is a central problem in modern financial markets, however finding and navigating optimal trajectories in this system is a non-trivial task. Many authors have developed exact analytical solutions by making simplifying assumptions regarding governing dynamics, however for practical feasibility and robustness, a more dynamic approach is needed to capture the spatial and temporal system complexity and adapt as intraday regimes change. This thesis aims to consolidate four key ideas: 1) the financial market as a complex adaptive system, where purposeful agents with varying system visibility collectively and simultaneously create and perceive their environment as they interact with it; 2) spin glass models as a tractable formalism to model phenomena in this complex system; 3) the multivariate Hawkes process as a candidate governing process for limit order book events; and 4) reinforcement learning as a framework for online, adaptive learning. Combined with the data and computational challenges of developing an efficient, machine-scale trading algorithm, we present a feasible scheme which systematically encodes these ideas. We first determine the efficacy of the proposed learning framework, under the conjecture of approximate Markovian dynamics in the equity market. We find that a simple lookup table Q-learning algorithm, with discrete state attributes and discrete actions, is able to improve post-trade implementation shortfall by adapting a typical static arrival-price volume trajectory with respect to prevailing market microstructure features streaming from the limit order book. To enumerate a scale-specific state space whilst avoiding the curse of dimensionality, we propose a novel approach to detect the intraday temporal financial market state at each decision point in the Q-learning algorithm, inspired by the complex adaptive system paradigm. A physical analogy to the ferromagnetic Potts model at thermal equilibrium is used to develop a high-speed maximum likelihood clustering algorithm, appropriate for measuring critical or near-critical temporal states in the financial system. State features are studied to extract time-scale-specific state signature vectors, which serve as low-dimensional state descriptors and enable online state detection. To assess the impact of agent interactions on the system, a multivariate Hawkes process is used to measure the resiliency of the limit order book with respect to liquidity-demand events of varying size. By studying the branching ratios associated with key quote replenishment intensities following trades, we ensure that the limit order book is expected to be resilient with respect to the maximum permissible trade executed by the agent. Finally we present a feasible scheme for unsupervised state discovery, state detection and online learning for high-frequency quantitative trading agents faced with a multifeatured, asynchronous market data feed. We provide a technique for enumerating the state space at the scale at which the agent interacts with the system, incorporating the effects of a live trading agent on limit order book dynamics into the market data feed, and hence the perceived state evolution.
LG2017
Valko, Michal. "Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning." Phd thesis, 2011. http://tel.archives-ouvertes.fr/tel-00643508.
Full textLiang, Yen-Lun, and 梁晏綸. "Empirical studies on the online learning algorithms based on combining weight noise injection and weight decay." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/81560444116082287065.
Full text國立中興大學
科技管理研究所
98
While injecting weight noise during training have been widely adopted in attaining fault tolerant neural newtorks, theoretical and empirical studies on the online algorithms developed based on these strategies have yet to be complete. In this thesis, we will investigate two important aspects in regard to the online learning algorithms based on combining weight noise injection and weight decay. Multiplicative weight noise and additive weight noise are considered seperately. The convergence behaviors and the performance of those learning algorithms are investigated via intensive computer simulations. It is found that (i) the online learning algorithm based on purely multiplicative weight noise injection does not converge, (ii) the algorithms combining weight noise injection and weight decay exhibit better convergence behaviors than their pure weight noise injection counterparts, and (iii) the neural networks attained by these algorithms combining weight noise injection and weight decay showing better fault tolerance abilities than the neural networks attained by the pure weight noise injection-based algorithms. The contributions of these results are two folds. First, part of these empirical results complement the recent findings from Ho, Leung & Sum on the convergence behaviors of the weight noise injection-based learning algorithms. Second, another part of the results which is in regard to the fault tolerance ability are new in the area. Finally, one should note that the results presented in this thesis also bring out an important message adding weight decay during training. Weight decay is not just can improve the convergence of an algorithm, but also can improve the weight noise tolerance ability of a neural network that is attained by these online algorithms.
Hsiao, Yi-Cheng, and 蕭義橙. "A statistical document classification system based on machine learning algorithms: Architecture and application in Facebook online discussion group." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/57322n.
Full text國立交通大學
教育研究所
106
The aim of this study is to develop a Chinese document classification systems for judging whether the content of the text is statistically relevant by means of machine learning algorithms. And the system is applied to the Facebook online discussion group in statistics course, classify posts and comments in the group is statistically relevant or not. Finally, this study will compare the reliability between machine classification and manual classification to explore whether the machine can achieve similar classification with humans. The experimental results show that the accuracy of the machine classification model is between .917 and .950, and the reliability of machine classification and manual classification is between .522 and .760, which means that the machine has high classification accuracy and have the potential to replace manual classification.
Chen, Jun-Hong, and 陳俊宏. "On the Prediction of Financial Time Series via Online Machine Learning Algorithms — An Example of S&P 500 Index Component Stocks." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/dacay2.
Full text臺北市立大學
資訊科學系碩士在職專班
104
This thesis studies online machine learning algorithms to the prediction problem for financial time series. Much research has been conducted in the field of time series analysis, in which many machine learning algorithms have been adopted to predict the trend of time series. However, as time goes by and more data become available, traditional machine learning algorithms need to merge the old data and the new one to a new training data, and re-train the model on the new training data. An online algorithm is one that can process its input piece-by-piece in a serial fashion, which makes it suitable for time series analysis. Therefore, in this study, we attempt to use several online machine learning algorithms to analyze the trend of S&P 500 index component stock prices. Experimental results show that there are small differences in terms of the accuracy between the offline and online algorithms; furthermore, the training time for the online learning algorithms is much faster than that for the offline algorithms, as the training data increases with time.
Singh, Ravinder. "Extracting Human Behaviour and Personality Traits from Social Media." Thesis, 2021. https://vuir.vu.edu.au/42639/.
Full textAzami, Sajjad. "Exploring fair machine learning in sequential prediction and supervised learning." Thesis, 2020. http://hdl.handle.net/1828/12098.
Full textGraduate
Turgeon, Stéphanie. "L’analyse appliquée du comportement en autisme et ses enjeux : une évaluation du potentiel de la technologie pour améliorer la pratique et la recherche." Thesis, 2021. http://hdl.handle.net/1866/25604.
Full textAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by significant deficits in social communication and social interactions and by the presence of restricted and repetitive behaviors or interests. Empirical evidence suggests that interventions based on applied behavior analysis (ABA) are the most effective for treating individuals with ASD. Nevertheless, interventions based on behavior analysis present some issues. In particular, intervention services are hard to access, knowledge about the underlying factors of the effectiveness of interventions is lacking and divergent perceptions about of ABA hamper the adoption of the science. This dissertation includes three studies in which technology is used to better understand or improve these issues regarding ABA. As part of my first study, the effects of a fully self-guided interactive web training (IWT) developed for teaching parents of children with ASD ABA-derived strategies to reduce their child's challenging behaviors were evaluated using a randomized waitlist trial. The results of this study support the effectiveness of the IWT for increasing the frequency of parents’ use of behavioral interventions as well as for reducing the frequency and severity of their child’s challenging behaviors. In contrast, no significant difference was observed for the measurement of parenting practices. Ethical and practical consideration regarding the dissemination of fully self-guided online trainings are discussed. The second study of my doctoral thesis aimed to show how to use machine learning algorithms to predict individuals who were most likely to improve following an intervention. Specifically, a demonstration of how to implement four machine learning algorithms to predict the participants from my first study who were the most likely to report a decrease in their child's iv challenging behaviors. This study argues that machine learning algorithms can be used with small samples to support clinicians’ and researchers’ decision making. The third study of my dissertation aimed to quantify the information about ABA published on four subforums of an internet forum; an online resource often used by families to identify potential interventions for their child. This goal was achieved through the use of a data mining procedure. The analyses showed that parents who visited the forum were exposed to a significant proportion of messages that disapproved of ABA for individuals with ASD or that inaccurately described its underlying principles, methods, procedures, or interventions. Together, the studies carried out as part of my doctoral dissertation highlight the benefits of technology to support assessments, interventions, and knowledge gains or transfer within psychosocial practices. As highlighted in the three studies of this dissertation, each of the tools used presents limitations and should therefore be used to support clinicians and researchers, and should not replace their interventions and clinical judgment. Future studies should continue to focus on the effectiveness of technological tools and on the underlying factors that will promote their use. Finally, researchers must reflect on the ethical considerations related to use of technology when working with humans.