000			03949cam a2200349 i 4500
001			18071335
005			20210316145409.0
008			140318s2014 enka b 001 0 eng
010			_a 2014002487
020			_a9781107024960 (hardback)
020			_a110702496X (hardback)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQ325.5 _b.K86 2014
082	0	0	_a006.310151252 KUN-S
100	1		_aKung, S. Y.
245	1	0	_aKernel methods and machine learning / _cS.Y. Kung
260			_aUnited Kingdom _bCambridge University Press _c2014
300			_a591 p.
365			_aGBP _b55.00.
504			_aIncludes bibliographical references (pages 561-577) and index.
505	8		_aMachine 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			_a"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			_a"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	_aSupport vector machines.
650		0	_aMachine learning.
650		0	_aKernel functions.
650		7	_aCOMPUTERS / Computer Vision & Pattern Recognition. _2bisacsh
856	4	2	_3Cover image _uhttp://assets.cambridge.org/97811070/24960/cover/9781107024960.jpg
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
955			_brl07 2014-03-18 _irl07 2014-03-18 ONIX to Dewey _axn08 2014-07-31 1 copy rec'd., to CIP ver. _arl00 2014-08-14 to SMA
999			_c22570 _d22570