Pattern recognition and machine learning (Record no. 37482)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03317 a2200241 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | ISURa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 140818b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780387310732 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English Language |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.399 |
| Item number | BIS |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Bishop , Christopher M. |
| 9 (RLIN) | 70190 |
| 245 ## - TITLE STATEMENT | |
| Title | Pattern recognition and machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Date of publication, distribution, etc | 2006 |
| Place of publication, distribution, etc | New York |
| Name of publisher, distributor, etc | Springer Science |
| 300 ## - PHYSICAL DESCRIPTION | |
| Other physical details | some Colour |
| Dimensions | 23 cm. |
| Extent | xx , 738 p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Information science and statistics. |
| 9 (RLIN) | 70191 |
| 500 ## - GENERAL NOTE | |
| General note | Introduction. Example : polynomial curve fitting ; Probability theory ; Model selection ; The curse of dimensionality Decision theory ; Information theory --<br/>Probability distributions. Binary vehicles ; Multinomial variables ; The Gaussian distribution ; The exponential family ; Nonparametric methods --<br/>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 --<br/>Linear models for classification. Discriminant functions ; Probabilistic generative models ; Probabilistic discrimitive models ; The Laplace approximation ; Bayesian logistic regression --<br/>Neural networks. Feed-forward network functions ; Network training ; Error backpropagation ; The Hessian matrix ; Regularization in neural networks ; Mixture density networks ; Bayesian neural networks --<br/>Kernel methods. Dual representations ; Constructing kernals ; Radial basis function networks ; Gaussian processes --<br/>Sparse Kernel machines. Maximum margin classifiers ; Relevance vector machines --<br/>Graphical models. Bayesian networks ; Conditional independence ; Markov random fields ; Inference in graphical models --<br/>Mixture models and EM. K-means clustering ; Mixtures of Gaussians ; An alternative view of EM ; The EM algorithm in general --<br/>Approximate inference. Variational inference ; Illustration : variational mixture of Gaussians ; Variational linear regression ; Exponential family distributions ; Local variational methods ; Variational logistic regression ; Expectation propagation --<br/>Sampling methods. Basic sampling algorithms ; Markov chain Monte Carlo ; Gibbs sampling ; Slice sampling ; The hybrid Monte Carlo algorithm ; Estimating the partition function --<br/>Continuous latent variables. Principal component analysis ; Probabilistic PCA ; Kernel PCA ; Nonlinear latent variable models --<br/>Sequential data. Markoc models ; Hidden Markov models ; Linear dynamical systems --<br/>Combining models. Bayesian model averaging ; Committees ; Boosting ; Tree-based models ; Conditional mixture models --<br/>Data sets --<br/>Probability distributions --<br/>Properties of matrices --<br/>Calculus of variations --<br/>Lagrange multipliers. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | <br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Pattern perception. . |
| 9 (RLIN) | 70192 |
| Topical term or geographic name as entry element | Pattern recognition systems |
| 9 (RLIN) | 70193 |
| Topical term or geographic name as entry element | Machine learning. |
| 9 (RLIN) | 69133 |
| Topical term or geographic name as entry element | Artificial intelligence |
| 9 (RLIN) | 1591 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Sheduled Reference |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Date checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Reference Collection | Applied Sciences Library | Applied Sciences Library | Reference Section | 22/02/2010 | 13000.00 | 1 | 621.399 BIS | 56710 | 01/11/2018 | 30/10/2018 | 24/06/2015 | Sheduled Reference | ||||
| Dewey Decimal Classification | Lending Collection | Applied Sciences Library | Applied Sciences Library | Lending Section | 05/04/2019 | 13450.50 | 621.399 BIS | 112942 | 06/04/2019 | 06/04/2019 | Lending Books | ||||||
| Dewey Decimal Classification | Lending Collection | Applied Sciences Library | Applied Sciences Library | Lending Section | 05/04/2019 | 13450.50 | 1 | 621.399 BIS | 112943 | 30/12/2020 | 13/02/2020 | 06/04/2019 | Lending Books |