Cherkassky / Mulier Learning from Data

Concepts, Theory, and Methods
2. Auflage 2008
ISBN: 978-0-470-14051-2
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, Band 1, 560 Seiten, E-Book

Reihe: Wiley - IEEE

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.

Autoren/Hrsg.

Cherkassky, Vladimir

Mulier, Filip M.

Über Autor(innen)

Vladimir CherKassky, PhD, is Professor of Electrical andComputer Engineering at the University of Minnesota. He isinternationally known for his research on neural networks andstatistical learning.
Filip Mulier, PhD, has worked in the software field for the lasttwelve years, part of which has been spent researching, developing,and applying advanced statistical and machine learning methods. Hecurrently holds a project management position.