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Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. 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. 760 pp. Englisch.
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Da: moluna, Greven, Germania
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Aggiungi al carrelloKartoniert / Broschiert. 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.
Lingua: Inglese
Editore: Springer-Verlag New York Inc., US, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 130,43
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Aggiungi al carrelloPaperback. Condizione: New. Softcover Reprint of the Original 1st 2006 ed.
Lingua: Inglese
Editore: SPRINGER NP EXCLUSIVE(CBS), 2009
ISBN 10: 1493938436 ISBN 13: 9781493938438
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Aggiungi al carrelloCondizione: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Lingua: Inglese
Editore: Springer New York, Springer US Aug 2016, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 80,24
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Aggiungi al carrelloTaschenbuch. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 760 pp. Englisch.
Lingua: Inglese
Editore: Springer-Verlag New York Inc., New York, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 114,79
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: preigu, Osnabrück, Germania
EUR 71,95
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Pattern Recognition and Machine Learning | Christopher M. Bishop | Taschenbuch | xx | Englisch | 2016 | Humana | EAN 9781493938438 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Da: Revaluation Books, Exeter, Regno Unito
EUR 127,85
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. revised edition. 738 pages. 9.75x6.75x1.50 inches. In Stock.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 86,97
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Lingua: Inglese
Editore: Springer-Verlag New York Inc., US, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Da: Rarewaves.com UK, London, Regno Unito
EUR 124,02
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Softcover Reprint of the Original 1st 2006 ed.
Da: Majestic Books, Hounslow, Regno Unito
EUR 80,11
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand pp. 758.