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Vladimir Cherkassky & Filip M. Mulier 
Learning from Data 
Concepts, Theory, and Methods

Ondersteuning
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.
€143.99
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Over de auteur

Vladimir Cher Kassky, Ph D, 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, Ph D, 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.
Taal Engels ● Formaat PDF ● Pagina’s 560 ● ISBN 9780470140512 ● Bestandsgrootte 4.9 MB ● Uitgeverij John Wiley & Sons ● Gepubliceerd 2008 ● Editie 2 ● Downloadbare 24 maanden ● Valuta EUR ● ID 2314296 ● Kopieerbeveiliging Adobe DRM
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