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Check Availability | Lawrence Library / Main Collection: Q327 .B52 2009 |

Author | Bishop, Christopher M. |

Title | Pattern recognition and machine learning / Christopher M. Bishop. |

Imprint | New York : Springer, ©2006. |

Description | xx, 738 p. : ill. (chiefly col.) ; 24 cm. |

Type of Material | Monograph |

Series | ( Information science and statistics ) |

Series | ( Information science and statistics.) |

Gen. Note | Textbook for graduates. |

Corrected at 8th printing 2009--t.p. verso. | |

Bibliography Note | Includes bibliographical references (p. 711-728) and index. |

Contents | 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 -- |

Contents: 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 -- | |

Contents: 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. | |

Summary | 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. |

Subject | Pattern perception. |

Pattern recognition systems. | |

Machine learning. | |

System Number | 000678674 |

ISBN | 0387310738 (hd. bd.) |

9780387310732 (hd. bd.) |

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