Pattern Recognition Machine Learning (häftad)
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Pattern Recognition Machine Learning (häftad)

Pattern Recognition Machine Learning

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Häftad Engelska, 2007-08-01
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The field of pattern recognition has undergone substantial development over the last ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. The practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had significant impact on both algorithms and applications. This new text book reflects these recent developments while providing a solid 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, and assumes no previous knowledge of pattern recognition or machine learning concepts. Familiarity with multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. TOC:Introduction.- Probability distributions.- Graphical models.- Mixture models and EM.- Approximate inference.- Continuous latent variables.- Sequential data.- Linear models for regression.- Linear models for classification.- Kernel methods.- Sparse kernal machines.- Neural networks.
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From the reviews: <p>"This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software <p>"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of a ~pattern recognitiona (TM) or a ~machine learninga (TM). a ] This book will serve as an excellent reference. a ] With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishopa (TM)s book is a useful introduction a ] and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) <p>"This book appears in the Information Science and Statistics Series commissioned by the publishers. a ] The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. a ] For course teachers there is ample backing which includes some 400 exercises. a ] it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) <p>"Bishop (Microsoft Research, UK) has prepared a marvelous book that providesa comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra a ] . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) <p>"The book is structured into 14 main parts and 5 appendices. a ] The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the booka (TM)s web site a ] ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) <p>"This new textbook by C. M. Bishop is a brilliant extension of his former book a ~Neural Networks for Pattern Recognitiona (TM). It is written for graduate students or scientists doing interdisciplinary work in related fields. a ] In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte fA1/4r Mathematik, Vol. 151 (3), 2007) <p>"Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. a ] Pattern Recognition and Machine Learning provides excellent intuitive descriptions andappropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. a ] I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008)

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Övrig information

Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.


Introduction * Probability distributions * Linear models for regression * Linear models for classification * Neural networks * Kernel methods * Sparse kernel machines * Graphical models * Mixture models and EM * Approximate inference * Sampling methods * Continuous latent variables * Sequential data * Combining models.