A01=Filip M. Mulier
A01=Vladimir Cherkassky
Author_Filip M. Mulier
Author_Vladimir Cherkassky
Category=PBT
Category=UY
Category=UYQM
conceptual
data provides
edition
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
first
framework
fundamental
general
interdisciplinary
learning
methods
new
pattern
principles
recognition
statistics
today
treatment
underlie
unified
various
Product details
- ISBN 9780471681823
- Weight: 916g
- Dimensions: 165 x 243mm
- Publication Date: 11 Sep 2007
- Publisher: John Wiley & Sons Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
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.
