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Author Bishop, Christopher M.

Title Pattern recognition and machine learning / Christopher M. Bishop
Published New York : Springer, 2006


Location Call no. Vol. Availability
 W'PONDS  006.4 Bis/Pra  AVAILABLE
 MELB  006.4 Bis/Pra  AVAILABLE
 MELB  006.4 Bis/Pra  AVAILABLE
 MELB  006.4 Bis/Pra  AVAILABLE
 MELB  006.4 Bis/Pra  AVAILABLE
 W'PONDS  006.4 Bis/Pra  AVAILABLE
Description xx, 738 pages : many fig. (chiefly color) ; 25 cm
Series Information science and statistics
Information science and statistics.
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
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.
Notes Textbook for graduates
Bibliography Includes bibliographical references and index
Subject Pattern perception.
Machine learning.
LC no. 2006922522
ISBN 0387310738 (