| 000 -LEADER |
|---|
| fixed length control field |
04142nam a22004695i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20140310151115.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
100721s2010 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781849960984 |
|
978-1-84996-098-4 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q337.5 |
|
|---|
| Classification number |
TK7882.P3 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.4 |
| Edition number |
23 |
| 264 #1 - |
|---|
| -- |
London : |
| -- |
Springer London, |
| -- |
2010. |
| 912 ## - |
|---|
| -- |
ZDB-2-SCS |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Abe, Shigeo. |
| Relator term |
author. |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Support Vector Machines for Pattern Classification |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc |
by Shigeo Abe. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XX, 473p. 228 illus., 114 illus. in color. |
| Other physical details |
online resource. |
| 440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
Advances in Pattern Recognition, |
| International Standard Serial Number |
2191-6586 |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Two-Class Support Vector Machines -- Multiclass Support Vector Machines -- Variants of Support Vector Machines -- Training Methods -- Kernel-Based Methods Kernel@Kernel-based method -- Feature Selection and Extraction -- Clustering -- Maximum-Margin Multilayer Neural Networks -- Maximum-Margin Fuzzy Classifiers -- Function Approximation. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various feature selection and extraction methods. Providing a unique perspective on the state of the art in SVMs, with a particular focus on classification, this thoroughly updated new edition includes a more rigorous performance comparison of classifiers and regressors. In addition to presenting various useful architectures for multiclass classification and function approximation problems, the book now also investigates evaluation criteria for classifiers and regressors. Topics and Features: Clarifies the characteristics of two-class SVMs through extensive analysis Discusses kernel methods for improving the generalization ability of conventional neural networks and fuzzy systems Contains ample illustrations, examples and computer experiments to help readers understand the concepts and their usefulness Includes performance evaluation using publicly available two-class data sets, microarray sets, multiclass data sets, and regression data sets (NEW) Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation (NEW) Covers sparse SVMs, an approach to learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning (NEW) Explores incremental training based batch training and active-set training methods, together with decomposition techniques for linear programming SVMs (NEW) Provides a discussion on variable selection for support vector regressors (NEW) An essential guide on the use of SVMs in pattern classification, this comprehensive resource will be of interest to researchers and postgraduate students, as well as professional developers. Dr. Shigeo Abe is a Professor at Kobe University, Graduate School of Engineering. He is the author of the Springer titles Neural Networks and Fuzzy Systems and Pattern Classification: Neuro-fuzzy Methods and Their Comparison. |
| 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 |
Artificial intelligence. |
|
|---|
| Topical term or geographic name as entry element |
Text processing (Computer science. |
|
|---|
| Topical term or geographic name as entry element |
Optical pattern recognition. |
|
|---|
| 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 |
Document Preparation and Text Processing. |
|
|---|
| Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
|
|---|
| Topical term or geographic name as entry element |
Control, Robotics, Mechatronics. |
| 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 |
|---|
| Display text |
Printed edition: |
| International Standard Book Number |
9781849960977 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
http://dx.doi.org/10.1007/978-1-84996-098-4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
E-Book |
