عنوان

Pattern recognition and machine learning

شابک

9780387310732

شماره

E2778

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Pattern recognition and machine learning

نام نخستين پديدآور

Christopher M. Bishop

محل نشرو پخش و غیره

New York

نام ناشر، پخش کننده و غيره

Springer

تاریخ نشرو بخش و غیره

2006

نام خاص و کميت اثر

xx, 738 p.

ساير جزييات

illustrations (chiefly color)

عنوان فروست

(Information science and statistics)

متن يادداشت

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

متن يادداشت

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

بدون عنوان

0

متن يادداشت

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

عنصر شناسه ای

Pattern perception

عنصر شناسه ای

Pattern recognition systems

عنصر شناسه ای

Machine learning

داده رابط بین فیلدها

a03

داده رابط بین فیلدها

a05

داده رابط بین فیلدها

a05

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Bishop, Christopher M.

کشور

Iran

سازمان

University of Tehran. Library of College of Science

تاريخ و ساعت مذاکره و دسترسي

UT_SCI_BL_DB_1002984_0001.pdf

فرمت انتشار

e

نوع ماده

BL

کد کاربرگه

278840

سطح دسترسي

a

تكميل شده

Y