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Añadir al carritoPaperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Publicado por Springer-Verlag New York Inc., 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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EUR 70,68
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Publicado por Springer-Verlag New York Inc., 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Añadir al carritoCondición: As New. Unread book in perfect condition.
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Añadir al carritoCondición: New. pp. 758.
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Publicado por Springer-Verlag New York Inc, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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Añadir al carritoPaperback. Condición: Brand New. revised edition. 738 pages. 9.75x6.75x1.50 inches. In Stock.
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Añadir al carritoCondición: New.
Publicado por Springer Verlag New York, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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Añadir al carritoTapa dura. Condición: Nuevo.
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Publicado por Springer-Verlag New York Inc., 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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Añadir al carritoCondición: New. 2016. Softcover reprint of the original 1st ed. 2006. Paperback. . . . . .
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Publicado por Springer-Verlag New York Inc., New York, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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Añadir al carritoPaperback. Condición: 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 multiple locations in the US or from the UK, depending on stock availability.
Publicado por Springer New York, Springer US Aug 2016, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
Librería: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Alemania
EUR 80,24
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Añadir al carritoTaschenbuch. Condición: 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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Añadir al carritoSoftcover. Condición: New. Special order direct from the distributor.
Publicado por Springer-Verlag New York Inc., 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
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Añadir al carritoCondición: New. 2016. Softcover reprint of the original 1st ed. 2006. Paperback. . . . . . Books ship from the US and Ireland.
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Añadir al carritoPaperback. Condición: Like New. Like New. book.
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Añadir al carritoCondición: 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.
Publicado por Springer New York, Springer US Aug 2016, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 84,73
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Añadir al carritoTaschenbuch. Condición: 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.
Publicado por Springer-Verlag New York Inc., New York, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 93,12
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Añadir al carritoPaperback. Condición: 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por Springer-Verlag New York Inc, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 133,83
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Añadir al carritoPaperback. Condición: Brand New. revised edition. 738 pages. 9.75x6.75x1.50 inches. In Stock.
Publicado por Springer-Verlag New York Inc., New York, 2016
ISBN 10: 1493938436 ISBN 13: 9781493938438
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 115,09
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Añadir al carritoPaperback. Condición: 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.
Librería: WorldofBooks, Goring-By-Sea, WS, Reino Unido
EUR 25,44
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Añadir al carritoHardback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Librería: BooksRun, Philadelphia, PA, Estados Unidos de America
EUR 36,24
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Añadir al carritoHardcover. Condición: Very Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Librería: WeBuyBooks, Rossendale, LANCS, Reino Unido
EUR 25,98
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Añadir al carritoCondición: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.