UID:
almafu_9959328847002883
Format:
1 online resource (xviii, 538 pages) :
,
illustrations
Edition:
2nd ed.
ISBN:
9780470140529
,
0470140526
,
9780470140512
,
0470140518
Content:
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.
Note:
Problem statement, classical approaches, and adaptive learning -- Regularization framework -- Statistical learning theory -- Nonlinear optimization strategies -- Methods for data reduction and dimensionality reduction -- Methods for regression -- Classification -- Support vector machines -- Noninductive inference and alternative learning formulations.
Additional Edition:
Print version: Cherkassky, Vladimir S. Learning from data. Hoboken, N.J. : IEEE Press : Wiley-Interscience, ©2007 ISBN 9780471681823
Language:
English
Keywords:
Electronic books.
;
Electronic books.
;
Electronic books.
DOI:
10.1002/9780470140529
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470140529
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470140529
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470140529
Bookmarklink
