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Descrizione libro Condizione: New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010. Codice articolo 1LAUHV002FLJ
Descrizione libro hardback. Condizione: New. Language: ENG. Codice articolo 9780387310732
Descrizione libro Condizione: New. New! This book is in the same immaculate condition as when it was published. Codice articolo 353-0387310738-new
Descrizione libro Condizione: New. Codice articolo ABLIING23Feb2215580171804
Descrizione libro Buch. Condizione: 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. Codice articolo 9780387310732
Descrizione libro Buch. Condizione: 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. Codice articolo 9780387310732
Descrizione libro Condizione: new. Book is in NEW condition. Satisfaction Guaranteed! Fast Customer Service!!. Codice articolo PSN0387310738
Descrizione libro Condizione: New. Codice articolo I-9780387310732
Descrizione libro Gebunden. Condizione: New. First text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. Presents approximate inference algorithms that permit fast approximate answers in situations where exact answers ar. Codice articolo 194599092
Descrizione libro Buch. Condizione: 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. Codice articolo 9780387310732