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About this product
- DescriptionOffering 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 techlogies. 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.
- Author BiographyS. Y. Kung is a Professor in the Department of Electrical Engineering at Princeton University. His research areas include VLSI array/parallel processors, system modeling and identification, wireless communication, statistical signal processing, multimedia processing, sensor networks, bioinformatics, data mining and machine learning.
- Author(s)S. Y. Kung
- PublisherCambridge University Press
- Date of Publication17/04/2014
- SubjectComputing: Textbooks & Study Guides
- Place of PublicationCambridge
- Country of PublicationUnited Kingdom
- ImprintCambridge University Press
- Content Note136 b/w illus. 21 tables
- Weight1360 g
- Width174 mm
- Height247 mm
- Spine29 mm
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