Learning from Data Concepts, Theory and Methods 2e

Vladimir Cherkassky/ Filip M. Mulier

ISBN 10: 0471681822 / ISBN 13: 9780471681823

Edité par IEEE, 2007

A propos de cet article

2nd edition. 538 pages. 9.75x6.50x1.25 inches. In Stock. N° de réf. du vendeur x-0471681822

Détails bibliographiques

Titre : Learning from Data Concepts, Theory and ...

Éditeur : IEEE

Date d'édition : 2007

Reliure : Hardcover

Etat : Brand New

Edition : 2ème Édition

A propos de ce titre

Synopsis :

Learning from Data An interdisciplinary framework for learning methodologies, covering statistics, neural networks, and fuzzy logic, Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data. Full description

Quatrième de couverture:

An interdisciplinary framework for learning methodologies now revised and updated

Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and pattern recognition can be applied showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science.

Since the first edition was published, the field of data–driven learning has experienced rapid growth. This Second Edition covers these developments with a completely revised chapter on support vector machines, a new chapter on noninductive inference and alternative learning formulations, and an in–depth discussion of the VC theoretical approach as it relates to other paradigms.

Complete with over one hundred illustrations, case studies, examples, and chapter summaries, Learning from Data accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.