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Learning from Data
Vladimir Cherkassky
€ 180.11
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Description for Learning from Data
Hardcover. 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. Num Pages: 538 pages, Illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 244 x 157 x 38. Weight in Grams: 906.
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.
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.
Product Details
Format
Hardback
Publication date
2007
Publisher
John Wiley and Sons Ltd United Kingdom
Number of pages
538
Condition
New
Number of Pages
560
Place of Publication
, United States
ISBN
9780471681823
SKU
V9780471681823
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About Vladimir Cherkassky
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. ... Read more
Reviews for Learning from Data
"I think Learning From Data is a very valuable volume. I will recommend it to my graduate students." (Journal of the American Statistical Association, March 2009) "The broad spectrum of information it offers is beneficial to many field of research. The selection of topics is good, and I believe that many researchers and practioners will find this book useful." ... Read more