Pattern recognition and machine learning / (Record no. 211)

MARC details
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 090311s2006 nyua b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2006922522
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780387310732
035 ## - SYSTEM CONTROL NUMBER
System control number (Sirsi) u1186
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
Modifying agency EG-CaNU
042 ## - AUTHENTICATION CODE
Authentication code ncode
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.4
Edition number 22
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Bishop, Christopher M.
9 (RLIN) 734
245 10 - TITLE STATEMENT
Title Pattern recognition and machine learning /
Statement of responsibility, etc. Christopher M. Bishop.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York :
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. 2006.
300 ## - PHYSICAL DESCRIPTION
Extent xx, 738 p. :
Other physical details ill. (some col.) ;
Dimensions 25 cm.
490 0# - SERIES STATEMENT
Series statement Information science and statistics
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1 Introduction -- 2 Probability Distributions -- 3 Linear Models for Regression -- 4 Linear Models for Classification -- 5 Neural Networks -- 6 Kernel Methods -- 7 Sparse Kernel Machines -- 8 Graphical Models -- 9 Mixture Models and EM -- 10 Approximate Inference -- 11 Sampling Methods -- 12 Continuous Latent Variables.
520 ## - SUMMARY, ETC.
Summary, etc. This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
596 ## -
-- 1
630 00 - SUBJECT ADDED ENTRY--UNIFORM TITLE
Uniform title CIT.
9 (RLIN) 14
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Pattern perception.
9 (RLIN) 735
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
9 (RLIN) 107
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Total Checkouts Total Renewals Full call number Barcode Date last seen Date last checked out Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main library Main library General Stacks 01/26/2020 IKRAA 3 10 006.4 / BI.P 2006 002289 02/28/2022 10/20/2021 1 11/24/2019 Books
    Dewey Decimal Classification     Main library Main library General Stacks 01/26/2020 PURCHASE 3   006.4 / BI.P 2006 002288 03/08/2025 12/25/2024 2 11/24/2019 Books