Kernel Methods and Machine Learning

Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

S. Y. Kung (Author)

9781107024960, Cambridge University Press

Hardback, published 17 April 2014

572 pages, 136 b/w illus. 21 tables
25.2 x 17.6 x 2.9 cm, 1.35 kg

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.

Subject Areas: Signal processing [UYS], Machine learning [UYQM], Computer science [UY], Electrical engineering [THR], Biomedical engineering [MQW]