MARC details
| 000 -LEADER |
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| fixed length control field |
03716cam a22003135a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
18071335 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20201128021508.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
140318s2014 enka frb f001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
110702496X (hardback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781107024960 (hardback) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Modifying agency |
EG-ScBUE |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.310151252 |
| Edition number |
22 |
| Item number |
KUN |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kung, S. Y. |
| Fuller form of name |
(Sun Yuan) |
| 9 (RLIN) |
38903 |
| 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 |
Cambridge : |
| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2014. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxiv, 591 p. : |
| Other physical details |
ill. ; |
| Dimensions |
26 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Index : p. 578-591. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Bibliography : p. 561-577. |
| 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. |
| Source of heading or term |
BUEsh |
| 9 (RLIN) |
6152 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| Source of heading or term |
BUEsh |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Kernel functions. |
| Source of heading or term |
BUEsh |
| 9 (RLIN) |
6153 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| Source of heading or term |
BUEsh |
| 9 (RLIN) |
2922 |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME |
|---|
| Source of heading or term |
BUEsh |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Resource For college |
Informatics and Computer Science |
| Arrived date list |
August2015 |
| -- |
December2015 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Call number prefix |
006.310151252 KUN |