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Kernel methods and machine learning.
- Publication Year :
- 2014
-
Abstract
- Summary: "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"-- Provided by publisher.
Details
- Language :
- English
- ISBN :
- 9781139176224
110702496X (hardback) - ISBNs :
- 9781139176224 and 110702496X
- Database :
- Jio Institute Digital Library Catalog
- Journal :
- Kernel methods and machine learning / S.Y. Kung, Princeton University.
- Notes :
- Includes bibliographical references (pages 561-577) and index.
- Publication Type :
- Book
- Accession number :
- jlc.oai.folio.org.fs00001072.697c3c03.45fc.4b74.ad76.80d1979b9628
- Document Type :
- Bibliographies; Non-fiction