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Pattern Recognition and Classification (Record no. 44340)

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001 - CONTROL NUMBER
control field sulb-eb0022248
003 - CONTROL NUMBER IDENTIFIER
control field BD-SySUS
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20160413122238.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 121026s2013 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461453239
-- 978-1-4614-5323-9
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-1-4614-5323-9
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q337.5
Classification number TK7882.P3
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQP
Source bicssc
Subject category code COM016000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.4
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Dougherty, Geoff.
Relator term author.
245 10 - TITLE STATEMENT
Title Pattern Recognition and Classification
Medium [electronic resource] :
Remainder of title An Introduction /
Statement of responsibility, etc. by Geoff Dougherty.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture New York, NY :
Name of producer, publisher, distributor, manufacturer Springer New York :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2013.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 196 p. 158 illus., 104 illus. in color.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 Overview -- 1.2 Classification -- 1.3 Organization of the Book -- Bibliography -- Exercises -- Chapter 2 Classification -- 2.1 The Classification Process -- 2.2 Features -- 2.3 Training and Learning -- 2.4 Supervised Learning and Algorithm Selection -- 2.5 Approaches to Classification -- 2.6 Examples -- 2.6.1 Classification by Shape -- 2.6.2 Classification by Size -- 2.6.3 More Examples -- 2.6.4 Classification of Letters -- Bibliography -- Exercises -- Chapter 3 Non-Metric Methods -- 3.1 Introduction -- 3.2 Decision Tree Classifier -- 3.2.1 Information, Entropy and Impurity -- 3.2.2 Information Gain -- 3.2.3 Decision Tree Issues -- 3.2.4 Strengths and Weaknesses -- 3.3 Rule-Based Classifier -- 3.4 Other Methods -- Bibliography -- Exercises -- Chapter 4 Statistical Pattern Recognition -- 4.1 Measured Data and Measurement Errors -- 4.2 Probability Theory -- 4.2.1 Simple Probability Theory -- 4.2.2 Conditional Probability and Bayes’ Rule -- 4.2.3 Naïve Bayes classifier -- 4.3 Continuous Random Variables -- 4.3.1 The Multivariate Gaussian -- 4.3.2 The Covariance Matrix -- 4.3.3 The Mahalanobis Distance -- Bibliography -- Exercises -- Chapter 5 Supervised Learning -- 5.1 Parametric and Non-Parametric Learning -- 5.2 Parametric Learning -- 5.2.1 Bayesian Decision Theory -- 5.2.2 Discriminant Functions and Decision Boundaries -- 5.2.3 MAP (Maximum A Posteriori) Estimator -- Bibliography -- Exercises -- Chapter 6 Non-Parametric Learning -- 6.1 Histogram Estimator and Parzen Windows -- 6.2 k-Nearest Neighbor (k-NN) Classification -- 6.3 Artificial Neural Networks (ANNs) -- 6.4 Kernel Machines -- Bibliography -- Exercises -- Chapter 7 Feature Extraction and Selection -- 7.1 Reducing Dimensionality -- 7.1.1 Pre-Processing -- 7.2 Feature Selection -- 7.2.1 Inter/Intra-Class Distance -- 7.2.2 Subset Selection -- 7.3 Feature Extraction -- 7.3.1 Principal Component Analysis (PCA) -- 7.3.2 Linear Discriminant Analysis (LDA) -- Bibliography -- Exercises -- Chapter 8 Unsupervised Learning -- 8.1 Clustering -- 8.2 k-Means Clustering -- 8.2.1 Fuzzy c-Means Clustering -- 8.3 (Agglomerative) Hierarchical Clustering -- Bibliography -- Exercises -- Chapter 9 Estimating and Comparing Classifiers -- 9.1 Comparing Classifiers and the No Free Lunch Theorem -- 9.1.2 Bias and Variance -- 9.2 Cross-Validation and Resampling Methods -- 9.2.1 The Holdout Method -- 9.2.2 k-Fold Cross-Validation -- 9.2.3 Bootstrap -- 9.3 Measuring Classifier Performance   -- 9.4 Comparing Classifiers -- 9.4.1 ROC curves -- 9.4.2 McNemar’s Test -- 9.4.3 Other Statistical Tests -- 9.4.4 The Classification Toolbox -- 9.5 Combining classifiers -- Bibliography -- Chapter 10 Projects -- 10.1 Retinal Tortuosity as an Indicator of Disease -- 10.2 Segmentation by Texture -- 10.3 Biometric Systems -- 10.3.1 Fingerprint Recognition -- 10.3.2 Face Recognition -- Bibliography -- Index.
520 ## - SUMMARY, ETC.
Summary, etc. The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
Topical term or geographic name as entry element Pattern recognition.
Topical term or geographic name as entry element Bioinformatics.
Topical term or geographic name as entry element Computational biology.
Topical term or geographic name as entry element Algorithms.
Topical term or geographic name as entry element Statistical physics.
Topical term or geographic name as entry element Computer Science.
Topical term or geographic name as entry element Pattern Recognition.
Topical term or geographic name as entry element Nonlinear Dynamics.
Topical term or geographic name as entry element Signal, Image and Speech Processing.
Topical term or geographic name as entry element Computer Appl. in Life Sciences.
Topical term or geographic name as entry element Algorithms.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9781461453222
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/978-1-4614-5323-9">http://dx.doi.org/10.1007/978-1-4614-5323-9</a>
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-- ZDB-2-SCS
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type

No items available.