Kernel Methods and Machine Learning
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$104.00
2024-06-13
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$108.00
2024-01-22
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2022-10-08

Kernel Methods and Machine Learning

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.

  • TitleKernel Methods and Machine Learning
  • BrandCambridge University Press
  • ManufacturerCambridge University Press
  • BindingHardcover
  • ProductGroupBook
  • ItemPartNumberYES11178478
  • UnitCount1
  • EANs9781107024960