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 折 $2,660
- 語言: 英文
- 頁數: 572
- 裝訂: Hardcover
- ISBN: 110702496X
- ISBN-13: 9781107024960
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相關分類:
Machine Learning 機器學習
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商品描述
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.