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LEADER 05560cam a2200757 a 4500

001 ocm71008143;

003 OCoLC;

005 20181101135606.0;

008 060818s2006 nyua b 001 0 eng c;

010 a| 2006922522;

019 a| 857226239;

020 a| 0387310738q| (hd. bd.);

020 a| 9780387310732q| (hd. bd.);

020 a| 1493938436q| (paperbk.);

020 a| 9781493938438q| (paperbk.);

024 3 a| 9780387310732;

035 a| (Sirsi) 99977027953;

035 a| 99977027953;

035 a| (OCoLC)71008143z| (OCoLC)857226239;

042 a| pcc;

050 00 a| Q327b| .B52 2006;

072 7 a| QA2| lcco;

080 a| 612.816.B62;

082 00 a| 006.42| 22;

084 a| 54.722| bcl;

084 a| 006.312| z;

100 1 a| Bishop, Christopher M.=| ^A809856;

245 10 a| Pattern recognition and machine learning /c| Christopher M. Bishop.;

300 a| xx, 738 pages :b| illustrations (chiefly color) ;c| 24 cm.;

336 a| textb| txt2| rdacontent;

337 a| unmediatedb| n2| rdamedia;

338 a| volumeb| nc2| rdacarrier;

490 1 a| Information science and statistics;

500 a| Textbook for graduates.;

504 a| Includes bibliographical references (pages 711-728) and index.;

505 0 a| 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.;

520 a| 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.;

650 0 a| Pattern perception.=| ^A22360;

650 0 a| Pattern recognition systems.=| ^A37220;

650 0 a| Machine learning.=| ^A145211;

650 6 a| Reconnaissance des formes (Informatique)v| Manuels d'enseignement supérieur.;

650 6 a| Apprentissage automatiquev| Manuels d'enseignement supérieur.;

650 6 a| Reconnaissance des formes (Informatique)v| Problèmes et exercices.;

650 6 a| Apprentissage automatiquev| Problèmes et exercices.;

650 6 a| Statistique mathématique.;

650 7 a| Machine learning.2| fast0| (OCoLC)fst01004795;

650 7 a| Pattern perception.2| fast0| (OCoLC)fst01055254;

650 7 a| Pattern recognition systems.2| fast0| (OCoLC)fst01055266;

650 17 a| Patroonherkenning.2| gtt;

650 17 a| Machine-learning.2| gtt;

650 7 a| Maschinelles Lernen.2| gnd;

650 7 a| Redes neurais.2| larpcal;

650 7 a| Reconhecimento de padrões.2| larpcal;

650 7 a| Aprendizado computacional.2| larpcal;

650 7 a| Machine learning.2| nasat;

650 07 a| Mustererkennung.2| swd;

650 4 a| Reconocimiento de modelos.;

650 4 a| Aprendizaje automático (Inteligencia artificial);

830 0 a| Information science and statistics.=| ^A590070;

949 i| 30372016681824o| jjlm;

960 o| 1s| 94.95t| Joyner48u| JETCz| USD;

596 a| 1;

998 a| 4876064;

999 a| Q327 .B52 2006w| LCc| 1i| 30372016681824d| 8/19/2025e| 4/11/2025f| 7/9/2025g| 2l| JGESm| JOYNERn| 5r| Ys| Yt| JGESBKu| 7/30/2018x| BOOKz| JSTACKSo| .STAFF. jjlm;

Pattern recognition and machine learning / Christopher M. Bishop.

Author/creator	Bishop, Christopher M.
Format	Book
Publication Info	New York : Springer, ©2006.
Description	xx, 738 pages : illustrations (chiefly color) ; 24 cm.
Subjects	Pattern perception. Pattern recognition systems. Machine learning. Reconnaissance des formes (Informatique)--Manuels d'enseignement supérieur. Apprentissage automatique--Manuels d'enseignement supérieur. Reconnaissance des formes (Informatique)--Problèmes et exercices. Apprentissage automatique--Problèmes et exercices. Statistique mathématique. Reconocimiento de modelos. Aprendizaje automático (Inteligencia artificial)

Series	Information science and statistics Information science and statistics. ^A590070
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.
Abstract	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.
General note	Textbook for graduates.
Bibliography note	Includes bibliographical references (pages 711-728) and index.
LCCN	2006922522
ISBN	0387310738 (hd. bd.)
ISBN	9780387310732 (hd. bd.)
ISBN	1493938436 (paperbk.)
ISBN	9781493938438 (paperbk.)
Standard identifier#	9780387310732

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Joyner	General Stacks	Q327 .B52 2006	✔ Available	Place Hold

Pattern recognition and machine learning / Christopher M. Bishop.

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