Kernel Methods and Machine Learning

S. Y. Kung

  • 出版商: Cambridge
  • 出版日期: 2014-04-17
  • 售價: $2,800
  • 貴賓價: 9.5$2,660
  • 語言: 英文
  • 頁數: 572
  • 裝訂: Hardcover
  • ISBN: 110702496X
  • ISBN-13: 9781107024960
  • 相關分類: Machine Learning 機器學習
  • 立即出貨 (庫存 < 3)

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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.