Pattern recognition and machine learning / Christopher M. Bishop.

Author
Bishop, Christopher M. [Browse]
Format
Book
Language
English
Published/​Created
  • New York : Springer, [2006]
  • ©2006
Description
xx, 738 pages : illustrations (chiefly color) ; 26 cm.

Availability

Copies in the Library

Location Call Number Status Location Service Notes
Engineering Library - Reserve Q327 .B52 2006 Browse related items Request
    Engineering Library - Stacks Q327 .B52 2006 Browse related items Request
      Lewis Library - Stacks Q327 .B52 2006 Browse related items Request

        Details

        Subject(s)
        Series
        Information science and statistics [More in this series]
        Summary note
        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.
        Bibliographic references
        Includes bibliographical references (p. 711-728) and index.
        Contents
        • Introduction
        • Example : polynomial curve fitting
        • Probability theory
        • Model selection
        • The curse of dimensionality
        • Decision theory
        • Information theory
        • Probability distributions
        • Binary variables
        • 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 discriminative 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 kernels
        • 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
        • Markov models
        • Hidden Markov models
        • Linear dynamical systems
        • Combining models
        • Bayesian model averaging
        • Committees
        • Boosting
        • Tree-based models
        • Conditional mixture models
        • Data sets
        • Properties of matrices
        • Calculus of variations
        • Lagrange multipliers.
        ISBN
        • 0387310738 (hardcover)
        • 9780387310732 (hardcover)
        • 1493938436 (paperback)
        • 9781493938438 (paperback)
        LCCN
        2006922522
        OCLC
        71008143
        International Article Number
        • 9780387310732
        Statement on language in description
        Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...
        Other views
        Staff view

        Supplementary Information