Kernel Methods and Machine Learning, Hardback 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.


  • Format: Hardback
  • Pages: 572 pages, 21 Tables, black and white; 4 Halftones, unspecified; 132 Line drawings, unspecified
  • Publisher: Cambridge University Press
  • Publication Date:
  • Category: Machine learning
  • ISBN: 9781107024960



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