Skip to search
Skip to main content
Catalog
Help
Feedback
Your Account
Library Account
Bookmarks
(
0
)
Search History
Search in
Keyword
Title (keyword)
Author (keyword)
Subject (keyword)
Title starts with
Subject (browse)
Author (browse)
Author (sorted by title)
Call number (browse)
search for
Search
Advanced Search
Bookmarks
(
0
)
Princeton University Library Catalog
Start over
Cite
Send
to
SMS
Email
EndNote
RefWorks
RIS
Printer
Bookmark
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)
Pattern perception
[Browse]
Pattern recognition systems
[Browse]
Machine learning
[Browse]
Pattern recognition systems
—
Textbooks
[Browse]
Machine learning
—
Textbooks
[Browse]
Pattern recognition systems
—
Problems, exercises, etc
[Browse]
Machine learning
—
Problems, exercises, etc
[Browse]
Mathematical statistics
[Browse]
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.
Show 83 more Contents items
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
Ask a Question
Suggest a Correction
Report Harmful Language
Supplementary Information
Other versions
Pattern recognition and machine learning / Christopher M. Bishop.
id
SCSB-9088756