Kernel Methods and Machine Learning
Kernel Methods and Machine Learning
NZD$159.95
inc GSTOffering 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.
- Covers various cutting edge techniques that can be used as a practical and accessible solution for a broad spectrum of application domains
- Discusses computationally efficient techniques suitable for green-IT technologies
- Explains the theory in an accessible, step-by-step manner, with problems and examples encouraging the reader to apply the theory in practice
Product details
April 2014Hardback
9781107024960
572 pages
252 × 176 × 29 mm
1.35kg
136 b/w illus. 21 tables
Available
Table of Contents
- 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.
