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Kernel Methods and Machine Learning 核方法与机器学习 S. Y. Kung 精装

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作者: S. Y. Kung
出版社: Cambridge University Press
ISBN: 9781107024960

出版时间: 2014-03
装帧: 精装
开本: 其他

作者: S. Y. Kung
出版社: Cambridge University Press

ISBN: 9781107024960
出版时间: 2014-03

装帧: 精装
开本: 其他

售价 1273.00

定价￥1273.00

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上书时间2024-03-28

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YB-71146

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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.
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