Dissertations / Theses on the topic 'Learning algorithms'
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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.
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Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
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Ameur, Foued ben Fredj. "Space-bounded learning algorithms /." Paderborn : Heinz Nixdorf Inst, 1996. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=007171235&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Hsu, Daniel Joseph. "Algorithms for active learning." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3404377.
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Thesis (Ph. D.)--University of California, San Diego, 2010.Title from first page of PDF file (viewed June 10, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 97-101).
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CESARI, TOMMASO RENATO. "ALGORITHMS, LEARNING, AND OPTIMIZATION." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/699354.
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This thesis covers some algorithmic aspects of online machine learning and optimization. In Chapter 1 we design algorithms with state-of-the-art regret guarantees for the problem dynamic pricing. In Chapter 2 we move on to an asynchronous online learning setting in which only some of the agents in the network are active at each time step. We show that when information is shared among neighbors, knowledge about the graph structure might have a significantly different impact on learning rates depending on how agents are activated. In Chapter 3 we investigate the online problem of multivariate non-concave maximization under weak assumptions on the regularity of the objective function. In Chapter 4 we introduce a new performance measure and design an efficient algorithm to learn optimal policies in repeated A/B testing.
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Janagam, Anirudh, and Saddam Hossen. "Analysis of Network Intrusion Detection System with Machine Learning Algorithms (Deep Reinforcement Learning Algorithm)." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17126.
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Sentenac, Flore. "Learning and Algorithms for Online Matching." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG005.
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Cette thèse se concentre principalement sur les problèmes d'appariement en ligne, où des ensembles de ressources sont alloués séquentiellement à des flux de demandes. Nous les traitons à la fois du point de vue de l'apprentissage en ligne et de l'analyse compétitive, toujours lorsqueEn ce qui concerne l'apprentissage en ligne, nous étudions comment la structure spécifique de l'appariement influence l'apprentissage dans la première partie, puis comment les effets de report dans le système affectent ses performances.En ce qui concerne l'analyse compétitive, nous étudions le problème de l'appariement en ligne dans des classes spécifiques de graphes aléatoires, dans un effort pour s'éloigner de l'analyse du pire cas.Enfin, nous explorons la manière dont l'apprentissage peut être exploité dans le problème d'ordonnancement des machinesThis thesis focuses mainly on online matching problems, where sets of resources are sequentially allocated to demand streams. We treat them both from an online learning and a competitive analysis perspective, always in the case when the input is stochastic.On the online learning side, we study how the specific matching structure influences learning in the first part, then how carry-over effects in the system affect its performance.On the competitive analysis side, we study the online matching problem in specific classes of random graphs, in an effort to move away from worst-case analysis.Finally, we explore how learning can be leveraged in the scheduling problem
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Thompson, Simon Giles. "Distributed boosting algorithms." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.
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Moon, Gordon Euhyun. "Parallel Algorithms for Machine Learning." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1561980674706558.
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Atiya, Amir Abu-Mostafa Yaser S. "Learning algorithms for neural networks /." Diss., Pasadena, Calif. : California Institute of Technology, 1991. http://resolver.caltech.edu/CaltechETD:etd-09232005-083502.
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Sanchez, Merchante Luis Francisco. "Learning algorithms for sparse classification." Phd thesis, Université de Technologie de Compiègne, 2013. http://tel.archives-ouvertes.fr/tel-00868847.
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This thesis deals with the development of estimation algorithms with embedded feature selection the context of high dimensional data, in the supervised and unsupervised frameworks. The contributions of this work are materialized by two algorithms, GLOSS for the supervised domain and Mix-GLOSS for unsupervised counterpart. Both algorithms are based on the resolution of optimal scoring regression regularized with a quadratic formulation of the group-Lasso penalty which encourages the removal of uninformative features. The theoretical foundations that prove that a group-Lasso penalized optimal scoring regression can be used to solve a linear discriminant analysis bave been firstly developed in this work. The theory that adapts this technique to the unsupervised domain by means of the EM algorithm is not new, but it has never been clearly exposed for a sparsity-inducing penalty. This thesis solidly demonstrates that the utilization of group-Lasso penalized optimal scoring regression inside an EM algorithm is possible. Our algorithms have been tested with real and artificial high dimensional databases with impressive resuits from the point of view of the parsimony without compromising prediction performances.
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Dalla, Libera Alberto. "Learning algorithms for robotics systems." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3422839.
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Robotics systems are now increasingly widespread in our day-life. For instance, robots have been successfully used in several fields, like, agriculture, construction, defense, aerospace, and hospitality. However, there are still several issues to be addressed for allowing the large scale deployment of robots. Issues related to security, and manufacturing and operating costs are particularly relevant. Indeed, differently from industrial applications, service robots should be cheap and capable of operating in unknown, or partially-unknown environments, possibly with minimal human intervention. To deal with these challenges, in the last years the research community focused on deriving learning algorithms capable of providing flexibility and adaptability to the robots. In this context, the application of Machine Learning and Reinforcement Learning techniques turns out to be especially useful. In this manuscript, we propose different learning algorithms for robotics systems. In Chapter 2, we propose a solution for learning the geometrical model of a robot directly from data, combining proprioceptive measures with data collected with a 2D camera. Besides testing the accuracy of the kinematic models derived with real experiments, we validate the possibility of deriving a kinematic controller based on the model identified. Instead, in Chapter 3, we address the robot inverse dynamics problem. Our strategy relies on the fact that the robot inverse dynamics is a polynomial function in a particular input space. Besides characterizing the input space, we propose a data-driven solution based on Gaussian Process Regression (GPR). Given the type of each joint, we define a kernel named Geometrically Inspired Polynomial (GIP) kernel, which is given by the product of several polynomial kernels. To cope with the dimensionality of the resulting polynomial, we use a variation of the standard polynomial kernel, named Multiplicative Polynomial kernel, further discussed in Chapter 6. Tests performed on simulated and real environments show that, compared to other data-driven solutions, the GIP kernel-based estimator is more accurate and data-efficient.
In Chapter 4, we propose a proprioceptive collision detection algorithm based on GPR. Compared to other proprioceptive approaches, we closely inspect the robot behaviors in quasi-static configurations, namely, configurations in which joint velocities are null or close to zero. Such configurations are particularly relevant in the Collaborative Robotics context, where humans and robots work side-by-side sharing the same environment. Experimental results performed with a UR10 robot confirm the relevance of the problem and the effectiveness of the proposed solution.
Finally, in Chapter 5, we present MC-PILCO, a model-based policy search algorithm inspired by the PILCO algorithm. As the original PILCO algorithm, MC-PILCO models the system evolution relying on GPR, and improves the control policy minimizing the expected value of a cost function. However, instead of approximating the expected cost by moment matching, MC-PILCO approximates the expected cost with a Monte Carlo particle-based approach; no assumption about the type of GPR model is necessary. Thus, MC-PILCO allows more freedom in designing the GPR models, possibly leading to better models of the system dynamics. Results obtained in a simulated environment show consistent improvements with respect to the original algorithm, both in terms of speed and success rate.
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Addanki, Ravichandra. "Learning generalizable device placement algorithms for distributed machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122746.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 47-50).
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph. We propose two key ideas in our approach: (1) we represent the policy as performing iterative placement improvements, rather than outputting a placement in one shot; (2) we use graph embeddings to capture relevant information about the structure of the computation graph, without relying on node labels for indexing. These ideas allow Placeto to train efficiently and generalize to unseen graphs. Our experiments show that Placeto requires up to 6.1 x fewer training steps to find placements that are on par with or better than the best placements found by prior approaches. Moreover, Placeto is able to learn a generalizable placement policy for any given family of graphs, which can then be used without any retraining to predict optimized placements for unseen graphs from the same family. This eliminates the large overhead incurred by prior RL approaches whose lack of generalizability necessitates re-training from scratch every time a new graph is to be placed.
by Ravichandra Addanki.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Philips, Petra Camilla, and petra philips@gmail com. "Data-Dependent Analysis of Learning Algorithms." The Australian National University. Research School of Information Sciences and Engineering, 2005. http://thesis.anu.edu.au./public/adt-ANU20050901.204523.
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This thesis studies the generalization ability of machine learning algorithms in a statistical setting. It focuses on the data-dependent analysis of the generalization performance of learning algorithms in order to make full use of the potential of the actual training sample from which these algorithms learn.¶
First, we propose an extension of the standard framework for the derivation of
generalization bounds for algorithms taking their hypotheses from random classes of functions. This approach is motivated by the fact that the function produced by a learning algorithm based on a random sample of data depends on this sample and is therefore a random function. Such an approach avoids the detour of the worst-case uniform bounds as done in the standard approach. We show that the mechanism which allows one to obtain generalization bounds for random classes in our framework is based on a “small complexity” of certain random coordinate
projections. We demonstrate how this notion of complexity relates to learnability
and how one can explore geometric properties of these projections in order to derive estimates of rates of convergence and good confidence interval estimates for the expected risk. We then demonstrate the generality of our new approach by presenting a range of examples, among them the algorithm-dependent compression schemes and the data-dependent luckiness
frameworks, which fall into our random subclass framework.¶
Second, we study in more detail generalization bounds for a specific algorithm which is of central importance in learning theory, namely the Empirical Risk Minimization algorithm (ERM). Recent results show that one can significantly improve the high-probability estimates for the convergence rates for empirical minimizers by a direct analysis of the ERM algorithm.
These results are based on a new localized notion of complexity of subsets of hypothesis functions with identical expected errors and are therefore dependent on the underlying unknown distribution. We investigate the extent to which one can estimate these high-probability convergence rates in a data-dependent manner. We provide an algorithm which computes a data-dependent upper bound for the expected error of empirical minimizers in terms of the “complexity” of data-dependent local subsets. These subsets are sets of functions of empirical errors of a given range and can be
determined based solely on empirical data.
We then show that recent direct estimates, which are essentially sharp estimates on the high-probability convergence rate for the ERM algorithm, can not be recovered universally from empirical data.
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Hadjifaradji, Saeed. "Learning algorithms for restricted neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0016/NQ48102.pdf.
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Amann, Notker. "Optimal algorithms for iterative learning control." Thesis, University of Exeter, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337751.
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Nambiar, Raghu. "Learning algorithms for adaptive digital filtering." Thesis, Durham University, 1993. http://etheses.dur.ac.uk/5544/.
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In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme.
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Hatzikos, Vasilis E. "Genetic algorithms into iterative learning control." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408314.
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Huang, Qingqing Ph D. Massachusetts Institute of Technology. "Efficient algorithms for learning mixture models." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107337.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 261-274).
We study the statistical learning problems for a class of probabilistic models called mixture models. Mixture models are usually used to model settings where the observed data consists of different sub-populations, yet we only have access to a limited number of samples of the pooled data. It includes many widely used models such as Gaussian mixtures models, Hidden Markov Models, and topic models. We focus on parametric learning: given unlabeled data generated according to a mixture model, infer about the parameters of the underlying model. The hierarchical structure of the probabilistic model leads to non-convexity of the likelihood function in the model parameters, thus imposing great challenges in finding statistically efficient and computationally efficient solutions. We start with a simple, yet general setup of mixture model in the first part. We study the problem of estimating a low rank M x M matrix which represents a discrete distribution over M2 outcomes, given access to sample drawn according to the distribution. We propose a learning algorithm that accurately recovers the underlying matrix using 9(M) number of samples, which immediately lead to improved learning algorithms for various mixture models including topic models and HMMs. We show that the linear sample complexity is actually optimal in the min-max sense. There are "hard" mixture models for which there exist worst case lower bounds of sample complexity that scale exponentially in the model dimensions. In the second part, we study Gaussian mixture models and HMMs. We propose new learning algorithms with polynomial runtime. We leverage techniques in probabilistic analysis to prove that worst case instances are actually rare, and our algorithm can efficiently handle all the non-worst case instances. In the third part, we study the problem of super-resolution. Despite the lower bound for any deterministic algorithm, we propose a new randomized algorithm which complexity scales only quadratically in all dimensions, and show that it can handle any instance with high probability over the randomization.
by Qingqing Huang.
Ph. D.
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Quattoni, Ariadna J. "Transfer learning algorithms for image classification." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53294.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 124-128).
An ideal image classifier should be able to exploit complex high dimensional feature representations even when only a few labeled examples are available for training. To achieve this goal we develop transfer learning algorithms that: 1) Leverage unlabeled data annotated with meta-data and 2) Exploit labeled data from related categories. In the first part of this thesis we show how to use the structure learning framework (Ando and Zhang, 2005) to learn efficient image representations from unlabeled images annotated with meta-data. In the second part we present a joint sparsity transfer algorithm for image classification. Our algorithm is based on the observation that related categories might be learnable using only a small subset of shared relevant features. To find these features we propose to train classifiers jointly with a shared regularization penalty that minimizes the total number of features involved in the approximation. To solve the joint sparse approximation problem we develop an optimization algorithm whose time and memory complexity is O(n log n) with n being the number of parameters of the joint model. We conduct experiments on news-topic and keyword prediction image classification tasks. We test our method in two settings: a transfer learning and multitask learning setting and show that in both cases leveraging knowledge from related categories can improve performance when training data per category is scarce. Furthermore, our results demonstrate that our model can successfully recover jointly sparse solutions.
by Ariadna Quattoni.
Ph.D.
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Zavaroni, Sofia. "Modulation Classification with Deep Learning Algorithms." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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L'attività di tesi è inquadrata nell'ambito del Deep Learning (DL), una potente tecnica di classificazione che ha un grande successo in molti domini applicativi. Tuttavia, il suo utilizzo nei sistemi di comunicazione non è stato ancora ben esplorato. Viene affrontato il problema dell'utilizzo di DL nei sistemi di comunicazione, in particolare per la classificazione della modulazione. Nello specifico, la rete neurale convoluzionale (CNN) viene utilizzata per completare l'attività di classificazione.
Le reti wireless di nuova generazione devono essere in grado di supportare comunicazioni ultra affidabili e a bassa latenza e gestire in modo intelligente i dispositivi Internet of Things (IoT) in un ambiente dinamico in tempo reale. Tali requisiti di comunicazione e intelligenza possono essere realizzati solo integrando nozioni fondamentali di intelligenza artificiale (AI) e machine learning (ML). In questa tesi, viene quindi introdotto l'uso di concetti completi di machine learning, in generale, e reti neurali artificiali (ANN), in particolare, e le loro potenziali applicazioni nelle comunicazioni wireless. Viene fornita una panoramica sulla varietà di problemi di comunicazione wireless che possono essere affrontati utilizzando le reti neurali (NN). Per ogni applicazione, viene presentata la motivazione principale per l'utilizzo delle NN insieme alle sfide associate, fornendo anche un esempio dettagliato per uno scenario di caso d'uso. Un’altra classe di NN di cui è opportuno approfondire il funzionamento è quella dell’Autoencoder (AE).
In seguito, questa tesi ha l'obiettivo di mostrare come utilizzare una rete neurale convoluzionale (CNN) per la classificazione della modulazione, generare forme d'onda sintetiche e alterate dal canale. Utilizzando le forme d'onda generate come dati di addestramento, viene addestrata una CNN per la classificazione della modulazione.
Tramite simulazioni, si è verificata la capacità dell'algoritmo di risolvere il problema in esame.
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Ellis, Kevin Ph D. (Kevin M. )Massachusetts Institute of Technology. "Algorithms for learning to induce programs." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130184.
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Thesis: Ph. D. in Cognitive Science, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, September, 2020Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 213-224).
The future of machine learning should have a knowledge representation that supports, at a minimum, several features: Expressivity, interpretability, the potential for reuse by both humans and machines, while also enabling sample-efficient generalization. Here we argue that programs-i.e., source code-are a knowledge representation which can contribute to the project of capturing these elements of intelligence. This research direction however requires new program synthesis algorithms which can induce programs solving a range of AI tasks. This program induction challenge confronts two primary obstacles: the space of all programs is infinite, so we need a strong inductive bias or prior to steer us toward the correct programs; and even if we have that prior, effectively searching through the vast combinatorial space of all programs is generally intractable. We introduce algorithms that learn to induce programs, with the goal of addressing these two primary obstacles. Focusing on case studies in vision, computational linguistics, and learning-to-learn, we develop an algorithmic toolkit for learning inductive biases over programs as well as learning to search for programs, drawing on probabilistic, neural, and symbolic methods. Together this toolkit suggests ways in which program induction can contribute to AI, and how we can use learning to improve program synthesis technologies.
by Kevin Ellis.
Ph. D. in Cognitive Science
Ph.D.inCognitiveScience Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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Ramakrishnan, Naveen. "Distributed Learning Algorithms for Sensor Networks." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1284991632.
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Pratap, Amrit Abu-Mostafa Yaser S. Abu-Mostafa Yaser S. "Adaptive learning algorithms and data cloning /." Diss., Pasadena, Calif. : Caltech, 2008. http://resolver.caltech.edu/CaltechETD:etd-05292008-231048.
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Lee, Jun won. "Relationships Among Learning Algorithms and Tasks." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2478.
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Metalearning aims to obtain knowledge of the relationship between the mechanism of learning and the concrete contexts in which that mechanisms is applicable. As new mechanisms of learning are continually added to the pool of learning algorithms, the chances of encountering behavior similarity among algorithms are increased. Understanding the relationships among algorithms and the interactions between algorithms and tasks help to narrow down the space of algorithms to search for a given learning task. In addition, this process helps to disclose factors contributing to the similar behavior of different algorithms. We first study general characteristics of learning tasks and their correlation with the performance of algorithms, isolating two metafeatures whose values are fairly distinguishable between easy and hard tasks. We then devise a new metafeature that measures the difficulty of a learning task that is independent of the performance of learning algorithms on it. Building on these preliminary results, we then investigate more formally how we might measure the behavior of algorithms at a ner grained level than a simple dichotomy between easy and hard tasks. We prove that, among all many possible candidates, the Classifi er Output Difference (COD) measure is the only one possessing the properties of a metric necessary for further use in our proposed behavior-based clustering of learning algorithms. Finally, we cluster 21 algorithms based on COD and show the value of the clustering in 1) highlighting interesting behavior similarity among algorithms, which leads us to a thorough comparison of Naive Bayes and Radial Basis Function Network learning, and 2) designing more accurate algorithm selection models, by predicting clusters rather than individual algorithms.
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Gupta, Pramod. "Robust clustering algorithms." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39553.
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One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across any different fields ranging from computational biology to social sciences to computer vision in part because they are simple and their output is easy to interpret. However, many of these algorithms lack any performance guarantees when the data is noisy, incomplete or has outliers, which is the case for most real world data. It is well known that standard linkage algorithms perform extremely poorly in presence of noise. In this work we propose two new robust algorithms for bottom-up agglomerative clustering and give formal theoretical guarantees for their robustness. We show that our algorithms can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also extend our algorithms to an inductive setting with similar guarantees, in which we randomly choose a small subset of points from a much larger instance space and generate a hierarchy over this sample and then insert the rest of the points to it to generate a hierarchy over the entire instance space. We then do a systematic experimental analysis of various linkage algorithms and compare their performance on a variety of real world data sets and show that our algorithms do much better at handling various forms of noise as compared to other hierarchical algorithms in the presence of noise.
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Dong, Lin. "A Comparison of Multi-instance Learning Algorithms." The University of Waikato, 2006. http://hdl.handle.net/10289/2453.
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Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms. This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems.
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Golea, Mostefa. "On efficient learning algorithms for neural networks." Thesis, University of Ottawa (Canada), 1993. http://hdl.handle.net/10393/6508.
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Inductive Inference Learning can be described in terms of finding a good approximation to some unknown classification rule f, based on a pre-classified set of training examples $\langle$x,f(x)$\rangle.$ One particular class of learning systems that has attracted much attention recently is the class of neural networks. But despite the excitement generated by neural networks, learning in these systems has proven to be a difficult task. In this thesis, we investigate different ways and means to overcome the difficulty of training feedforward neural networks. Our goal is to come up with efficient learning algorithms for new classes (or architectures) of neural nets. In the first approach, we relax the constraint of fixed architecture adopted by most neural learning algorithms. We describe two constructive learning algorithms for two-layer and tree-like networks. In the second approach, we adopt the "probably approximately correct" (PAC) learning model and we look for positive learnability results by restricting the distribution generating the training examples, the connectivity of the networks, and/or the weight values. This enables us to identify new classes of neural networks that are efficiently learnable in the chosen setting. In the third and final approach, we look at the problem of learning in neural networks from the average case point of view. In particular, we investigate the average case behavior of the well known clipped Hebb rule when learning different neural networks with binary weights. The arguments given for the "efficient learnability" range from extensive simulations to rigorous mathematical proofs.
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Agache, Mariana. "Families of estimator-based stochastic learning algorithms." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0027/MQ52379.pdf.
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Mitchell, Brian. "Prepositional phrase attachment using machine learning algorithms." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412729.
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Pasteris, S. U. "Efficient algorithms for online learning over graphs." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.
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In this thesis we consider the problem of online learning with labelled graphs, in particular designing algorithms that can perform this problem quickly and with low memory requirements. We consider the tasks of Classification (in which we are asked to predict the labels of vertices) and Similarity Prediction (in which we are asked to predict whether two given vertices have the same label). The first half of the thesis considers non- probabilistic online learning, where there is no probability distribution on the labelling and we bound the number of mistakes of an algorithm by a function of the labelling's complexity (i.e. its "naturalness"), often the cut- size. The second half of the thesis considers probabilistic machine learning in which we have a known probability distribution on the labelling. Before considering probabilistic online learning we first analyse the junction tree algorithm, on which we base our online algorithms, and design a new ver- sion of it, superior to the otherwise current state of the art. Explicitly, the novel contributions of this thesis are as follows: • A new algorithm for online prediction of the labelling of a graph which has better performance than previous algorithms on certain graph and labelling families. • Two algorithms for online similarity prediction on a graph (a novel problem solved in this thesis). One performs very well whilst the other not so well but which runs exponentially faster. • A new (better than before, in terms of time and space complexity) state of the art junction tree algorithm, as well as an application of it to the problem of online learning in an Ising model. • An algorithm that, in linear time, finds the optimal junction tree for online inference in tree-structured Ising models, the resulting online junction tree algorithm being far superior to the previous state of the art. All claims in this thesis are supported by mathematical proofs.
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Li, Xia. "Travel time prediction using ensemble learning algorithms." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/53358/.
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In the research area of travel time prediction, the existing studies mainly focus on aggregated travel time prediction (without distinguishing vehicle types) or travel time prediction for passenger vehicles. The travel time prediction for freight transportation has not received enough attention from researchers. Only a few relevant studies can be found in the literature, and the proposed methods are usually very simple and lack comparisons with more advanced methods. Although many believed that a better prediction model can be developed using more advanced techniques such as artificial neural networks or autoregressive conditional heteroscedastic models, it is usually difficult and costly to train these models and the model interpretability is poor. There is a demand for `off-the-shelf' methods with good performance, ease of implementation and good model interpretability. Thus, the aims of this thesis are: (1) developing some `off-the-shelf' data-driven methods to predict travel time for freight transportation; (2) creating a comprehensive understanding of how the developed methods can be more effectively applied for general travel time prediction problems. Its two main contributions are: (1) it develops data-driven travel time prediction methods for freight transportation by utilising freight vehicles' trajectory data; (2) it investigates the relation between features and performance and discovers the combinatorial effects of features under the effects of different noise processes and different model fitting strategies. The experimental results show that useful features can be mined from the trajectory data to enhance the travel time prediction for freight transportation. The developed methods outperform some of the state-of-the-art data-driven methods.
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Aslam, Javed A. "Noise tolerant algorithms for learning and searching." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36533.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 109-112).
by Javed Alexander Aslam.
Ph.D.
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Sloan, Robert Hal. "Computational learning theory : new models and algorithms." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/38339.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Includes bibliographical references (leaves 116-120).
by Robert Hal Sloan.
Ph.D.
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Betke, Margrit. "Learning and vision algorithms for robot navigation." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11402.
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Sonntag, Dag. "Chain Graphs : Interpretations, Expressiveness and Learning Algorithms." Doctoral thesis, Linköpings universitet, Databas och informationsteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125921.
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Probabilistic graphical models are currently one of the most commonly used architectures for modelling and reasoning with uncertainty. The most widely used subclass of these models is directed acyclic graphs, also known as Bayesian networks, which are used in a wide range of applications both in research and industry. Directed acyclic graphs do, however, have a major limitation, which is that only asymmetric relationships, namely cause and effect relationships, can be modelled between their variables. A class of probabilistic graphical models that tries to address this shortcoming is chain graphs, which include two types of edges in the models representing both symmetric and asymmetric relationships between the variables. This allows for a wider range of independence models to be modelled and depending on how the second edge is interpreted, we also have different so-called chain graph interpretations. Although chain graphs were first introduced in the late eighties, most research on probabilistic graphical models naturally started in the least complex subclasses, such as directed acyclic graphs and undirected graphs. The field of chain graphs has therefore been relatively dormant. However, due to the maturity of the research field of probabilistic graphical models and the rise of more data-driven approaches to system modelling, chain graphs have recently received renewed interest in research. In this thesis we provide an introduction to chain graphs where we incorporate the progress made in the field. More specifically, we study the three chain graph interpretations that exist in research in terms of their separation criteria, their possible parametrizations and the intuition behind their edges. In addition to this we also compare the expressivity of the interpretations in terms of representable independence models as well as propose new structure learning algorithms to learn chain graph models from data.
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Johansson, Samuel, and Karol Wojtulewicz. "Machine learning algorithms in a distributed context." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148920.
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Interest in distributed approaches to machine learning has increased significantly in recent years due to continuously increasing data sizes for training machine learning models. In this thesis we describe three popular machine learning algorithms: decision trees, Naive Bayes and support vector machines (SVM) and present existing ways of distributing them. We also perform experiments with decision trees distributed with bagging, boosting and hard data partitioning and evaluate them in terms of performance measures such as accuracy, F1 score and execution time. Our experiments show that the execution time of bagging and boosting increase linearly with the number of workers, and that boosting performs significantly better than bagging and hard data partitioning in terms of F1 score. The hard data partitioning algorithm works well for large datasets where the execution time decrease as the number of workers increase without any significant loss in accuracy or F1 score, while the algorithm performs poorly on small data with an increase in execution time and loss in accuracy and F1 score when the number of workers increase.
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Shen, Chenyang. "Regularized models and algorithms for machine learning." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/195.
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Multi-lable learning (ML), multi-instance multi-label learning (MIML), large network learning and random under-sampling system are four active research topics in machine learning which have been studied intensively recently. So far, there are still a lot of open problems to be figured out in these topics which attract worldwide attention of researchers. This thesis mainly focuses on several novel methods designed for these research tasks respectively. Then main difference between ML learning and traditional classification task is that in ML learning, one object can be characterized by several different labels (or classes). One important observation is that the labels received by similar objects in ML data are usually highly correlated with each other. In order to exploring this correlation of labels between objects which might be a key issue in ML learning, we consider to require the resulting label indicator to be low rank. In the proposed model, nuclear norm which is a famous convex relaxation of intractable matrix rank is introduced to label indicator in order to exploiting the underlying correlation in label domain. Motivated by the idea of spectral clustering, we also incorporate information from feature domain by constructing a graph among objects based on their features. Then with partial label information available, we integrate them together into a convex low rank based model designed for ML learning. The proposed model can be solved efficiently by using alternating direction method of multiplier (ADMM). We test the performance on several benchmark ML data sets and make comparisons with the state-of-art algorithms. The classification results demonstrate the efficiency and effectiveness of the proposed low rank based methods. One step further, we consider MIML learning problem which is usually more complicated than ML learning: besides the possibility of having multiple labels, each object can be described by multiple instances simultaneously which may significantly increase the size of data. To handle the MIML learning problem we first propose and develop a novel sparsity-based MIML learning algorithm. Our idea is to formulate and construct a transductive objective function for label indicator to be learned by using the method of random walk with restart that exploits the relationships among instances and labels of objects, and computes the affinities among the objects. Then sparsity can be introduced in the labels indicator of the objective function such that relevant and irrelevant objects with respect to a given class can be distinguished. The resulting sparsity-based MIML model can be given as a constrained convex optimization problem, and it can be solved very efficiently by using the augmented Lagrangian method (ALM). Experimental results on benchmark data have shown that the proposed sparse-MIML algorithm is computationally efficient, and effective in label prediction for MIML data. We demonstrate that the performance of the proposed method is better than the other testing MIML learning algorithms. Moreover, one big concern of an MIML learning algorithm is computational efficiency, especially when figuring out classification problem for large data sets. Most of the existing methods for solving MIML problems in literature may take a long computational time and have a huge storage cost for large MIML data sets. In this thesis, our main aim is to propose and develop an efficient Markov Chain based learning algorithm for MIML problems. Our idea is to perform labels classification among objects and features identification iteratively through two Markov chains constructed by using objects and features respectively. The classification of objects can be obtained by using labels propagation via training data in the iterative method. Because it is not necessary to compute and store a huge affinity matrix among objects/instances, both the storage and computational time can be reduced significantly. For instance, when we handle MIML image data set of 10000 objects and 250000 instances, the proposed algorithm takes about 71 seconds. Also experimental results on some benchmark data sets are reported to illustrate the effectiveness of the proposed method in one-error, ranking loss, coverage and average precision, and show that it is competitive with the other methods. In addition, we consider the module identification from large biological networks. Nowadays, the interactions among different genes, proteins and other small molecules are becoming more and more significant and have been studied intensively. One general way that helps people understand these interactions is to analyze networks constructed from genes/proteins. In particular, module structure as a common property of most biological networks has drawn much attention of researchers from different fields. However, biological networks might be corrupted by noise in the data which often lead to the miss-identification of module structure. Besides, some edges in network might be removed (or some nodes might be miss-connected) when improper parameters are selected which may also affect the module identified significantly. In conclusion, the module identification results are sensitive to noise as well as parameter selection of network. In this thesis, we consider employing multiple networks for consistent module detection in order to reduce the effect of noise and parameter settings. Instead of studying different networks separately, our idea is to combine multiple networks together by building them into tensor structure data. Then given any node as prior label information, tensor-based Markov chains are constructed iteratively for identification of the modules shared by the multiple networks. In addition, the proposed tensor-based Markov chain algorithm is capable of simultaneously evaluating the contribution from each network. It would be useful to measure the consistency of modules in the multiple networks. In the experiments, we test our method on two groups of gene co-expression networks from human beings. We also validate biological meaning of modules identified by the proposed method. Finally, we introduce random under-sampling techniques with application to X-ray computed tomography (CT). Under-sampling techniques are realized to be powerful tools of reducing the scale of problem especially for large data analysis. However, information loss seems to be un-avoidable which inspires different under-sampling strategies for preserving more useful information. Here we focus on under-sampling for the real-world CT reconstruction problem. The main motivation is to reduce the total radiation dose delivered to patient which has arisen significant clinical concern for CT imaging. We compare two popular regular CT under-sampling strategies with ray random under-sampling. The results support the conclusion that random under-sampling always outperforms regular ones especially for the high down-sampling ratio cases. Moreover, based on the random ray under-sampling strategy, we propose a novel scatter removal method which further improves performance of ray random under-sampling in CT reconstruction.
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Choudhury, A. "Fast machine learning algorithms for large data." Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/45907/.
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Traditional machine learning has been largely concerned with developing techniques for small or modestly sized datasets. These techniques fail to scale up well for large data problems, a situation becoming increasingly common in today’s world. This thesis is concerned with the problem of learning with large data. In particular, it considers solving the three basic tasks in machine learning, viz., classification, regression and density approximation. We develop fast memory- efficient algorithmics for kernel machine training and deployment. These include considering efficient preprocessing steps for speeding up existing training algorithms as well as developing a general purpose framework for machine learning using kernel methods. Emphasis is placed on the development of computationally efficient greedy schemes which leverage state-of-the-art techniques from the field of numerical linear algebra. The algorithms presented here underline a basic premise that it is possible to efficiently train a kernel machine on large data, which generalizes well and yet has a sparse expansion leading to improved runtime performance. Empirical evidence is provided in support of this premise throughout the thesis.
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Karlsson, Daniel. "Hyperparameter optimisation using Q-learning based algorithms." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78096.
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Machine learning algorithms have many applications, both for academic and industrial purposes. Examples of applications are classification of diffraction patterns in materials science and classification of properties in chemical compounds within the pharmaceutical industry. For these algorithms to be successful they need to be optimised, part of this is achieved by training the algorithm, but there are components of the algorithms that cannot be trained. These hyperparameters have to be tuned separately. The focus of this work was optimisation of hyperparameters in classification algorithms based on convolutional neural networks. The purpose of this thesis was to investigate the possibility of using reinforcement learning algorithms, primarily Q-learning, as the optimising algorithm. Three different algorithms were investigated, Q-learning, double Q-learning and a Q-learning inspired algorithm, which was designed during this work. The algorithms were evaluated on different problems and compared to a random search algorithm, which is one of the most common optimisation tools for this type of problem. All three algorithms were capable of some learning, however the Q-learning inspired algorithm was the only one to outperform the random search algorithm on the test problems. Further, an iterative scheme of the Q-learning inspired algorithm was implemented, where the algorithm was allowed to refine the search space available to it. This showed further improvements of the algorithms performance and the results indicate that similar performance to the random search may be achieved in a shorter period of time, sometimes reducing the computational time by up to 40%.Maskininlärningsalgoritmer har många tillämpningsområden, både akademiska och inom industrin. Exempel på tillämpningar är, klassificering av diffraktionsmönster inom materialvetenskap och klassificering av egenskaper hos kemiska sammansättningar inom läkemedelsindustrin. För att dessa algoritmer ska prestera bra behöver de optimeras. En del av optimering sker vid träning av algoritmerna, men det finns komponenter som inte kan tränas. Dessa hyperparametrar måste justeras separat. Fokuset för det här arbetet var optimering av hyperparametrar till klassificeringsalgoritmer baserade på faltande neurala nätverk. Syftet med avhandlingen var att undersöka möjligheterna att använda förstärkningsinlärningsalgoritmer, främst ''Q-learning'', som den optimerande algoritmen. Tre olika algoritmer undersöktes, ''Q-learning'', dubbel ''Q-learning'' samt en algoritm inspirerad av ''Q-learning'', denna utvecklades under arbetets gång. Algoritmerna utvärderades på olika testproblem och jämfördes mot resultat uppnådda med en slumpmässig sökning av hyperparameterrymden, vilket är en av de vanligare metoderna för att optimera den här typen av algoritmer. Alla tre algoritmer påvisade någon form av inlärning, men endast den ''Q-learning'' inspirerade algoritmen presterade bättre än den slumpmässiga sökningen. En iterativ implemetation av den ''Q-learning'' inspirerade algoritmen utvecklades också. Den iterativa metoden tillät den tillgängliga hyperparameterrymden att förfinas mellan varje iteration. Detta medförde ytterligare förbättringar av resultaten som indikerade att beräkningstiden i vissa fall kunde minskas med upp till 40% jämfört med den slumpmässiga sökningen med bibehållet eller förbättrat resultat.
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Westerlund, Fredrik. "CREDIT CARD FRAUD DETECTION (Machine learning algorithms)." Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136031.
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Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This essay investigates the supervised approach, where two algorithms (Hellinger Distance Decision Tree (HDDT) and Random Forest) are evaluated on a real life dataset of 284,807 transactions. Under those circumstances, the main purpose is to develop a “well-functioning” model with a reasonable capacity to categorize transactions as fraudulent or legit. As the data is heavily unbalanced, reducing the false-positive rate is also an important part when conducting research in the chosen area. In conclusion, evaluated algorithms present a fairly similar outcome, where both models have the capability to distinguish the classes from each other. However, the Random Forest approach has a better performance than HDDT in all measures of interest.
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Valiveti, Radhakrishna Carleton University Dissertation Engineering Electrical. "Learning algorithms for data retrieval and storage." Ottawa, 1990.
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Agache, Mariana Carleton University Dissertation Computer Science. "Families of estimator-based stochastic learning algorithms." Ottawa, 2000.
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Dragone, Paolo. "Coactive Learning Algorithms for Constructive Preference Elicitation." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3581/1/thesis-final.pdf.
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Preference-based decision problems often involve choosing one among a large set of options, making common tasks like buying a car or a domestic appliance very challenging for a customer to handle on her own. This is especially true when
buying online, where the amount of available options is humongous, and expert advice is yet limited. Recommender systems have become essential computational tools for aiding users in this endeavor. Recommender systems represent one of the most successful applications of artificial intelligence.
In the last decades, several recommendation approaches have been proposed for different types of applications, from assisted browsing of product catalogs to personalization of results in search engines. Depending on the application, the
job of the recommender system may be to recommend a satisfying option for the given context, as in finding the next best song to play, as opposed to helping the user in finding an optimal instance, e.g. when looking for an
apartment. The former is generally handled by data-driven approaches, such as collaborative filtering and contextual bandits, while in the latter case data is usually scarce, making it necessary to employ specialized algorithms for
preference elicitation. Preference elicitation algorithms interactively build a utility model of the user preferences and then recommend the instances with the highest utility. Preference elicitation is especially effective when recommending infrequently purchased items, such as professional work tools, electronic devices and other products that can be explicitly stored e.g. in the database of an e-commerce website. Standard preference elicitation
algorithms, however, struggle when the options are so numerous that cannot even be explicitly enumerated, and instead need to be represented implicitly as a collection of variables and constraints. Indeed, when a customer wants to
configure a product by putting several components together, e.g. for a custom personal computer, the option space is combinatorial and grows exponentially with the number of components, making it impractical to store every single
feasible combination explicitly. This is an example of constructive decision problem, in which an object has to be synthesized on the basis of the preferences of the customer and the constraints over the configuration domain.
Constructive problems such as product configuration have traditionally been addressed by specialized configurator systems, which guide the user through the configuration process component by component and check whether the user choices are consistent with the set of feasibility constraints. Over the years, however, the limitations of product configurators for mass customization have become
apparent. With the growing scale of configuration problems, product configurators have become more difficult for non-experts to use and ultimately do not provide relief against the "mass confusion" caused by the sheer amount
of choice. Research in this field has progressively been integrating recommendation technologies into configuration systems, in order to make them more flexible and easy to use. Preference elicitation in product configuration
has been attempted as well but still remains a challenge.
We propose a generic framework for preference elicitation in constructive domains, that is able to scale to large combinatorial problems better than existing techniques. Our constructive preference elicitation framework is based
on online structured prediction, a machine learning technique that deals with sequential decision problems over structured objects. By combining online structured prediction and state-of-the-art constraint solvers we can efficiently learn user utility models and make increasingly better recommendations for complex preference-based constructive problems such as product configuration. In
particular, we favor the use of coactive learning, an online structured prediction framework for preference learning. Coactive learning is particularly well suited for constructive preference elicitation as it may be seen as a
cooperation between the user and the system. The user and the systems interact through "coactive" feedback: after each recommendation, the user provides a modification that makes it slightly better for her preferences. This type of
feedback is very flexible and can be acquired both explicitly and implicitly from the user actions. Coactive learning also comes with theoretical convergence
guarantees and a set of ready-made extensions for many related problems such as learning in a multi-user setting and learning with approximate constraint solvers.
In this thesis we detail our coactive learning approach to constructive preference elicitation, and propose extensions for scaling up to very large constructive problems and personalizing the utility model. We also applied our framework to two important classes of constructive
preference elicitation problems, namely layout synthesis and product bundling. The former is a design process for arranging objects into a given space, while the latter is a kind of product configuration problem in which the object to
configure is a package of different products and services. Within the product bundling application, we also performed an extensive validation involving real participants, which highlights the practical benefits of our approach.
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Li, Xiao. "Regularized adaptation : theory, algorithms, and applications /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5928.
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Chalup, Stephan Konrad. "Incremental learning with neural networks, evolutionary computation and reinforcement learning algorithms." Thesis, Queensland University of Technology, 2001.
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Dinh, The Canh. "Distributed Algorithms for Fast and Personalized Federated Learning." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30019.
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The significant increase in the number of cutting-edge user equipment (UE) results in the phenomenal growth of the data volume generated at the edge. This shift fuels the booming trend of an emerging technique named Federated Learning. In contrast to traditional methods in which data is collected and processed centrally, FL builds a global model from contributions of UE's model without sending private data then effectively ensures data privacy. However, FL faces challenges in non-identically distributed (non-IID) data, communication cost, and convergence rate.
Firstly, we propose first-order optimization FL algorithms named FedApprox and FEDL to improve the convergence rate. We propose FedApprox exploiting proximal stochastic variance-reduced gradient methods and extract insights from convergence conditions via the algorithm’s parameter control. We then propose FEDL to handle heterogeneous UE data and characterize the trade-off between local computation and global communication. Experimentally, FedApprox outperforms vanilla FedAvg while FEDL outperforms FedApprox and FedAvg.
Secondly, we consider the communication between edges to be more costly than local computational overhead. We propose DONE, a distributed approximate Newton-type algorithm for communication-efficient federated edge learning. DONE approximates Newton direction using classical Richardson iteration on each edge. Experimentally, DONE attains a comparable performance to Newton’s method and outperforms first-order algorithms.
Finally, we address the non-IID issue by proposing pFedMe, a personalized FL algorithm using Moreau envelopes. pFedMe achieves quadratic speedup for strongly convex and sublinear speedup of order 2/3 for smooth nonconvex objectives. We then propose FedU, a Federated Multitask Learning algorithm using Laplacian regularization to leverage the relationships among the users' models. Experimentally, pFedMe excels FedAvg and Per-FedAvg while FedU outperforms pFedMe and MOCHA.
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Tu, Zhuozhuo. "Towards Robust and Reliable Machine Learning: Theory and Algorithms." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28832.
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Machine learning models, especially deep neural networks, have achieved impressive performance across a variety of domains including image classification, natural language processing, and speech recognition. However, recent examples have shown that these models are susceptible to test-time shift such as adversarial attacks or distributional shift. Additionally, machine learning algorithms require having access to personal data, and the learned model can be discriminatory with respect to minority social groups, raising privacy and fairness risks. To tackle these issues, in this thesis, we study several topics on robustness and reliability in machine learning, with a focus on generalization, adversarial examples, distributional robustness and fairness (privacy).
We start with the generalization problem in recurrent neural networks. We propose new generalization bounds for recurrent neural networks based on matrix 1-norm and Fisher-Rao norm. Our bound has no explicit dependency on the size of networks and can potentially explain the effect of noise training on generalization of recurrent neural networks as demonstrated by our empirical results. We then move forward to dataset shift robustness, which involves adversarial examples and distributional shift. For adversarial examples, we theoretically analyze the adversarially robust generalization properties of machine learning models. For distributional shift, we focus on learning a robust model and propose new algorithms to solve Wasserstein distributionally robust optimization problem which apply to arbitrary level of robustness and general loss functions. Lastly, to ensure both privacy and fairness, we present a fairness-aware federated learning framework and provide an efficient and provably convergent algorithm to solve it. Experimental results show that our method can lead to significant benefits in practice in terms of both accuracy and fairness.
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Wang, Gang. "Solution path algorithms : an efficient model selection approach /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANGG.
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Si, Si, and 斯思. "Cross-domain subspace learning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44912912.
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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.
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