Kernel Methods Machine Learning S. Y. Kung Hardcover Cambridge Un… 9781107024960

Description: Kernel Methods and Machine LearningS. Y. Kung Cambridge University Press Hardcover Unused and unread, cosmetic imperfections such as scuffs, creases or knocks. Stamped 'damaged' by publisher to a non-text page. EAN: 9781107024960 Published 17/04/2014 Language: English 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. 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. DispatchIn stock here - same-day dispatch from England. My SKU: 3161026RefundsNo-hassle refunds are always available if your book is not as expected.Terms and Conditions of SaleSorry - no collections. All sales are subject to extended Terms and Conditions of Sale as well as the Return Policy and Payment Instructions. Visit my eBay Store for details andmany more books. Template layout and design, "JNC Academic Books", "needbooks", Copyright © JNC INC. Designated trademarks, layouts and brands are the property of their respective owners. All Rights Reserved.

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