Pattern Recognition and Machine Learning

Abstract

his book provides an introduction to the eld of pattern recognition and machine earning. It gives an overview of several asic and advanced topics in machine earning theory. The book is definitely aluable to scientists and engineers who re involved in developing machine learnng tools applied to signal and image proessing applications. This book is also uitable for courses on machine learning nd pattern recognition, designed for adanced undergraduates or PhD students. o previous knowledge of machine learnng concepts or algorithms is assumed, but eaders need some knowledge of calculus nd linear algebra. The book is compleented by a great deal of additional suports for instructors and students. The suports include solutions to the exercises in ach chapter, the example data sets used hroughout the book and the forthcoming ompanion book that deals with practical nd software implementations of the key lgorithms. A strong point of this book is hat the mathematical expressions or algoithms are usually accompanied with colrful graphs and figures. This definitely elps to communicate the concepts much etter to the students or the interested reearchers than pure description of the algoithms. The book also provides an interestng short biography of the key scientists nd mathematicians who have contributed istorically to the basic mathematical conepts and methods in each chapter. This book consists of 14 chapters covring the basic concepts of the probability heory, classical linear regression, binary iscrimination or classification, neural netorks, and advanced topics such as kernel ethods, Bayesian graphical models, ariational inference, Monte Carlo samling methods, hidden Markov models, nd fusion of classifiers. Chapter 1 introuces the basics of machine learning and lassical pattern recognition by introducing wo examples—recognition of handwritten igits and polynomial curve fitting. Using

DOI: 10.1117/1.2819119

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@article{Bishop2007PatternRA, title={Pattern Recognition and Machine Learning}, author={Christopher M. Bishop and Nasser M. Nasrabadi}, journal={J. Electronic Imaging}, year={2007}, volume={16}, pages={049901} }