000			03716cam a22003135a 4500
001			18071335
005			20201128021508.0
008			140318s2014 enka frb f001 0 eng d
020			_a110702496X (hardback)
020			_a9781107024960 (hardback)
040			_aDLC _beng _cDLC _dEG-ScBUE
082	0	4	_a006.310151252 _222 _bKUN
100	1		_a Kung, S. Y. _q(Sun Yuan) _938903
245	1	0	_aKernel methods and machine learning / _cS. Y. Kung.
260			_aCambridge : _bCambridge University Press, _c2014.
300			_axxiv, 591 p. : _bill. ; _c26 cm.
500			_aIndex : p. 578-591.
504			_aBibliography : p. 561-577.
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. _2BUEsh _96152
650		0	_aMachine learning. _2BUEsh
650		0	_aKernel functions. _2BUEsh _96153
650		0	_aMachine learning _2BUEsh _92922
651			_2BUEsh
653			_bCOMSCI _cAugust2015 _cDecember2015
942			_2ddc _k006.310151252 KUN
999			_c20542 _d20514