Pattern Recognition And Machine Learning

Author: Christopher M. Bishop
Publisher: Springer
ISBN: 9781493938438
Size: 41.94 MB
Format: PDF, Kindle
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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Pattern Recognition And Machine Learning

Author: Christopher M. Bishop
Publisher: Springer Verlag
ISBN: 9780387310732
Size: 11.40 MB
Format: PDF, ePub, Mobi
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The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.

Pattern Recognition And Machine Learning

Author: Christopher M. Bishop
Publisher: Springer Verlag
ISBN: 9780387310732
Size: 47.18 MB
Format: PDF, ePub, Mobi
View: 3759
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This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

Bayesian Reasoning And Machine Learning

Author: David Barber
Publisher: Cambridge University Press
ISBN: 0521518148
Size: 52.22 MB
Format: PDF
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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

The Nature Of Statistical Learning Theory

Author: Vladimir N. Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475724403
Size: 54.91 MB
Format: PDF, Kindle
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The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.

Netlab

Author: Ian Nabney
Publisher: Springer Science & Business Media
ISBN: 9781852334406
Size: 61.86 MB
Format: PDF, Kindle
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This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems. Features of particular interest include: - A NETLAB toolbox which is freely available - Worked examples, demonstration programs and over 100 graded exercises - Cutting edge research made accessible for the first time in a highly usable form - Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian Processes Although primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models. "...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research." Christopher Bishop, Microsoft Research, Cambridge, UK "...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining" Jack Cowan, Mathematics Department, University of Chicago, US "If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book." Keith Worden, University of Sheffield, UK

Machine Learning

Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 0262018020
Size: 10.50 MB
Format: PDF, ePub
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Support Vector Machines

Author: Ingo Steinwart
Publisher: Springer Science & Business Media
ISBN: 0387772421
Size: 31.83 MB
Format: PDF, ePub, Docs
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Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.

Pattern Recognition And Neural Networks

Author: Brian D. Ripley
Publisher: Cambridge University Press
ISBN: 9780521717700
Size: 36.89 MB
Format: PDF
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Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.