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"Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. ... Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-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. ... 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
"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 pattern recognition or machine learning . ... This book will serve as an excellent reference. ... With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop s book is a useful introduction ... and a valuable reference for the principle techniques used in these fields." --Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007
"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
"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 pattern recognition or machine learning . ... This book will serve as an excellent reference. ... With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop s book is a useful introduction ... and a valuable reference for the principle techniques used in these fields." --Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007
"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
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- ÉditeurSpringer-Verlag New York Inc.
- Date d'édition2011
- ISBN 10 0387310738
- ISBN 13 9780387310732
- ReliureRelié
- Numéro d'édition1
- Nombre de pages738
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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Description du livre Etat : New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010. N° de réf. du vendeur 1LAUHV002FLJ
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Pattern Recognition and Machine Learning
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Description du livre hardback. Etat : New. Language: ENG. N° de réf. du vendeur 9780387310732
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Description du livre Etat : New. New! This book is in the same immaculate condition as when it was published. N° de réf. du vendeur 353-0387310738-new
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Description du livre Etat : New. N° de réf. du vendeur ABLIING23Feb2215580171804
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Description du livre Etat : new. N° de réf. du vendeur FrontCover0387310738
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Pattern Recognition and Machine Learning
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Description du livre Buch. Etat : Neu. Neuware -Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past 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. Also, 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 pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of 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. 778 pp. Englisch. N° de réf. du vendeur 9780387310732
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Pattern Recognition and Machine Learning
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Description du livre Buch. Etat : Neu. Neuware -Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past 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. Also, 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 pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of 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. 778 pp. Englisch. N° de réf. du vendeur 9780387310732
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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Description du livre Hardcover. Etat : new. Brand New Copy. N° de réf. du vendeur BBB_new0387310738
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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Description du livre Hardcover. Etat : new. Prompt service guaranteed. N° de réf. du vendeur Clean0387310738
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Pattern Recognition And Machine Learning
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Description du livre Etat : new. Book is in NEW condition. Satisfaction Guaranteed! Fast Customer Service!!. N° de réf. du vendeur PSN0387310738
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