MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03949cam a2200349 i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
18071335 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20210316145409.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
140318s2014 enka b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2014002487 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781107024960 (hardback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
110702496X (hardback) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 |
| Item number |
.K86 2014 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.310151252 KUN-S |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kung, S. Y. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Kernel methods and machine learning / |
| Statement of responsibility, etc. |
S.Y. Kung |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
United Kingdom |
| Name of publisher, distributor, etc. |
Cambridge University Press |
| Date of publication, distribution, etc. |
2014 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
591 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
GBP |
| Price amount |
55.00. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references (pages 561-577) and index. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Machine generated contents note: Part I. Machine Learning and Kernel Vector Spaces: 1. Fundamentals of machine learning; 2. Kernel-induced vector spaces; Part II. Dimension-Reduction: Feature Selection and PCA/KPCA: 3. Feature selection; 4. PCA and Kernel-PCA; Part III. Unsupervised Learning Models for Cluster Analysis: 5. Unsupervised learning for cluster discovery; 6. Kernel methods for cluster discovery; Part IV. Kernel Ridge Regressors and Variants: 7. Kernel-based regression and regularization analysis; 8. Linear regression and discriminant analysis for supervised classification; 9. Kernel ridge regression for supervised classification; Part V. Support Vector Machines and Variants: 10. Support vector machines; 11. Support vector learning models for outlier detection; 12. Ridge-SVM learning models; Part VI. Kernel Methods for Green Machine Learning Technologies: 13. Efficient kernel methods for learning and classifcation; Part VII. Kernel Methods and Statistical Estimation Theory: 14. Statistical regression analysis and errors-in-variables models; 15: Kernel methods for estimation, prediction, and system identification; Part VIII. Appendices: Appendix A. Validation and test of learning models; Appendix B. kNN, PNN, and Bayes classifiers; References; Index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors"-- |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Provides an overview of the broad spectrum of applications and problem formulations for kernel-based unsupervised and supervised learning methods. The dimension of the original vector space, along with its Euclidean inner product, often proves to be highly inadequate for complex data analysis. In order to provide a more e |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Support vector machines. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Kernel functions. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
COMPUTERS / Computer Vision & Pattern Recognition. |
| Source of heading or term |
bisacsh |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Cover image |
| Uniform Resource Identifier |
<a href="http://assets.cambridge.org/97811070/24960/cover/9781107024960.jpg">http://assets.cambridge.org/97811070/24960/cover/9781107024960.jpg</a> |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) |
|---|
| Withdrawn status |
|
| 955 ## - COPY-LEVEL INFORMATION (RLIN) |
|---|
| Book number/undivided call number, CCAL (RLIN) |
rl07 2014-03-18 |
| Copy status, CST (RLIN) |
rl07 2014-03-18 ONIX to Dewey |
| Classification number, CCAL (RLIN) |
xn08 2014-07-31 1 copy rec'd., to CIP ver. |
| -- |
rl00 2014-08-14 to SMA |
