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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.Read more
- 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
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- Date Published: June 2014
- format: Hardback
- isbn: 9781107024960
- length: 572 pages
- dimensions: 252 x 176 x 29 mm
- weight: 1.35kg
- contains: 136 b/w illus. 21 tables
- availability: In stock
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
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