Bokus
Vladimir Cherkassky, Filip M. Mulier - Learning from Data, Inbunden

Inbunden, Engelska, 2007

Learning from Data

Concepts, Theory, and Methods

Av Vladimir Cherkassky, Filip M. Mulier

Del i serien IEEE Press

1934 kr

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Beskrivning
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.
Produktinformation
  • Utgivningsdatum: 2007-08-24
  • Mått: 165 x 243 x 34 mm
  • Vikt: 916 g
  • Format: Inbunden
  • Språk: Engelska
  • Serie: IEEE Press
  • Antal sidor: 560
  • Upplaga: 2
  • Förlag: John Wiley & Sons Inc
  • ISBN: 9780471681823
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Mer om författaren

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.

Recensioner i media

"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." (Technometrics, May 2008)"The authors have succeeded in summarizing some of the recent trends and future challenges in different learning methods, including enabling technologies and some interesting practical applications." (Computing Reviews, May 22, 2008)

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