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Kernel Methods and Machine Learning - 9781107024960
Un libro in lingua di Kung S. Y. edito da Cambridge Univ Pr, 2014
- € 87.90
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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"--
Informazioni bibliografiche
- Titolo del Libro in lingua: Kernel Methods and Machine Learning
- Lingua: English
- Autore: Kung S. Y.
- Editore: Cambridge Univ Pr
- Collana: Cambridge Univ Pr (Hardcover)
- Data di Pubblicazione: 23 Giugno '14
- Genere: COMPUTERS
- Argomenti : Support vector machines Machine learning Kernel functions
- Pagine: 591
- Dimensioni mm: 254 x 152 x 25
- ISBN-10: 110702496X
- EAN-13: 9781107024960