Learning from Data: Concepts, Theory, and Methods Second Edition

Cherkassky, Vladimir, Mulier, Filip M.

ISBN 10: 0471681822 / ISBN 13: 9780471681823

Editore: Wiley-IEEE Press, 2007

*Price HAS BEEN REDUCED by 10% until Monday, June 23. Order now for BEST SAVINGS* 2nd edition, 560 pp., hardcover, previous owner's name neatly inked to the title page, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Codice articolo ZB1317152

Riassunto:

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book 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 fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Informazioni sull?autore:

Vladimir CherKassky, PhD, is Professor of Electrical and Computer Engineering at the University of Minnesota. He is internationally known for his research on neural networks and statistical learning.

Filip Mulier, PhD, has worked in the software field for the last twelve years, part of which has been spent researching, developing, and applying advanced statistical and machine learning methods. He currently holds a project management position.

Dati bibliografici

Titolo: Learning from Data: Concepts, Theory, and ...
Casa editrice: Wiley-IEEE Press
Data di pubblicazione: 2007
Legatura: Rilegato
Condizione: Fine
Edizione: seconda edizione