pattern recognition machine learning de bishop christopher (64 resultados)

Paperback. Condición: Very Good. Cover and edges may have some wear.

Condición: Good. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

Hardcover. Condición: Near fine. Corrected at 8th printing 2009. Large octavo with black and goldenrod glossy boards; xx, 738 pages: illustrations (chiefly color); index; 24 cm. The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a 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. Contents: Introduction. Example: polynomial curve fitting; Probability theory; Model selection; The curse of dimensionality Decision theory; Information theory. Probability distributions. Binary vehicles; Multinomial variables; The Gaussian distribution; The exponential family; Nonparametric methods. Linear models for regression. Linear basis function models; The bias-variance decomposition; Bayesian linear regression; Bayesian model comparison; The evidence approximation; Limitations of fixed basis functions. Linear models for classification. Discriminant functions; Probabilistic generative models; Probabilistic discrimitive models; The Laplace approximation; Bayesian logistic regression. Neural networks. Feed-forward network functions; Network training; Error backpropagation; The Hessian matrix; Regularization in neural networks; Mixture density networks; Bayesian neural networks. Kernel methods. Dual representations; Constructing kernals; Radial basis function networks; Gaussian processes. Sparse Kernel machines. Maximum margin classifiers; Relevance vector machines. Graphical models. Bayesian networks; Conditional independence; Markov random fields; Inference in graphical models. Mixture models and EM. K-means clustering; Mixtures of Gaussians; An alternative view of EM; The EM algorithm in general. Approximate inference. Variational inference; Illustration: variational mixture of Gaussians; Variational linear regression; Exponential family distributions; Local variational methods; Variational logistic regression; Expectation propagation. Sampling methods. Basic sampling algorithms; Markov chain Monte Carlo; Gibbs sampling; Slice sampling; The hybrid Monte Carlo algorithm; Estimating the partition function. Continuous latent variables. Principal component analysis; Probabilistic PCA; Kernel PCA; Nonlinear latent variable models. Sequential data. Markoc models; Hidden Markov models; Linear dynamical systems. Combining models. Bayesian model averaging; Committees; Boosting; Tree-based models; Conditional mixture models. Data sets. Probability distributions. Properties of matrices. Calculus of variations. Lagrange multipliers. // **Somewhat heavy item. Additional shipping fees may be needed for expedited or international orders. Please inquire**. Near fine.

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1850grams, ISBN:9780387310732.

Condición: New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.

Condición: New.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

Condición: New. In.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New. In.

Paperback. Condición: Sehr gut. Gebraucht - Sehr gut Sg - leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - 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.

Kartoniert / Broschiert. Condición: 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.

Condición: New. pp. 758.

Taschenbuch. 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.

Condición: New. pp. 758.

Condición: New.

Condición: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition.Please note:-May include library or rental stickers, stamps, or markings.-Supplemental materials e.g., CDs, access codes, inserts are not guaranteed.-Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. -Sourced from donation centers; authenticity not verified with publisher. Your satisfaction is our top priority! If you have any questions or concerns about your order, please donât hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading!

Condición: New.

Condición: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Item may be missing bundled media.

Taschenbuch. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 760 pp. Englisch.

PF. Condición: New.

Condición: Very Good. Ships SAME or NEXT business day. We Ship to APO/FPO addr. Choose EXPEDITED shipping and receive in 2-5 business days within the United States. See our member profile for customer support contact info. We have an easy return policy.

Taschenbuch. Condición: 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.

Paperback. Condición: As New. Information Science and Statistics; 738 pages; A very nice, exceptionally clean copy. Doesn't look used at all!

Condición: New. pp. 758.

paperback. Condición: New. 2006th Edition. Ships in a BOX from Central Missouri! Ships same or next business day. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condición: As New. Unread book in perfect condition.