Kernel Methods and Machine Learning
About this book
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.
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Information
Table of contents
- Cover
- Half-title page
- Title page
- Copyright page
- Dedication
- Contents
- Preface
- Part I Machine learning and kernel vector spaces
- Part II Dimension-reduction: PCA/KPCA and feature selection
- Part III Unsupervised learning models for cluster analysis
- Part IV Kernel ridge regressors and variants
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- Part VII Kernel methods and statistical estimation theory
- Part VIII Appendices
- References
- Index