kernel methods machine learning de kung (21 résultats)

Etat : New.

Etat : New.

Etat : Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1400grams, ISBN:9781107024960.

Etat : New. In.

Etat : New.

Hardcover. Etat : new. Hardcover. 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. Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred problems and real-world examples, it is an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Etat : New. pp. 495 Index.

Hardcover. Etat : Brand New. 591 pages. 10.00x6.00x1.00 inches. In Stock.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Etat : As New. Unread book in perfect condition.

Etat : New. Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science. Num Pages: 572 pages, 136 b/w illus. 21 tables. BIC Classification: UYQM; UYQP. Category: (U) Tertiary Education (US: College). Dimension: 255 x 174 x 32. Weight in Grams: 1354. . 2014. 1st Edition. hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Etat : Like New. Like New. book.

Etat : As New. Unread book in perfect condition.

Etat : New. Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science. Num Pages: 572 pages, 136 b/w illus. 21 tables. BIC Classification: UYQM; UYQP. Category: (U) Tertiary Education (US: College). Dimension: 255 x 174 x 32. Weight in Grams: 1354. . 2014. 1st Edition. hardcover. . . . .

Hardcover. Etat : new. Hardcover. 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. Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred problems and real-world examples, it is an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : Brand New. 591 pages. 10.00x6.00x1.00 inches. In Stock. This item is printed on demand.

Hardcover. Etat : new. Hardcover. 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. Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred problems and real-world examples, it is an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Etat : New. Print on Demand pp. 495 136 Illus.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred problems and real-world examples, it is an essential resource for graduate students and profession.

Etat : New. PRINT ON DEMAND pp. 495.

Buch. Etat : Neu. Kernel Methods and Machine Learning | S. Y. Kung | Buch | Gebunden | Englisch | 2014 | Cambridge University Press | EAN 9781107024960 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.