عنوان

Pattern recognition and machine learning

(Number (ISBN

9780387310732

Number

E2778

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Pattern recognition and machine learning

First Statement of Responsibility

Christopher M. Bishop

Place of Publication, Distribution, etc.

New York

Name of Publisher, Distributor, etc.

Springer

Date of Publication, Distribution, etc.

2006

Specific Material Designation and Extent of Item

xx, 738 p.

Other Physical Details

illustrations (chiefly color)

Series Title

(Information science and statistics)

Text of Note

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

Text of Note

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

0

Text of 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

Entry Element

Pattern perception

Entry Element

Pattern recognition systems

Entry Element

Machine learning

a03

a05

a05

Class number

Book number

Bishop, Christopher M.

Country

Iran

Agency

University of Tehran. Library of College of Science

Date and Hour of Consultation and Access

UT_SCI_BL_DB_1002984_0001.pdf

e

BL

278840

a

Y