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.
Sobre o autor
Vladimir Cher Kassky, Ph D, is Professor of Electrical andComputer 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.
Língua Inglês ● Formato PDF ● Páginas 560 ● ISBN 9780470140512 ● Tamanho do arquivo 4.9 MB ● Editora John Wiley & Sons ● Publicado 2008 ● Edição 2 ● Carregável 24 meses ● Moeda EUR ● ID 2314296 ● Proteção contra cópia Adobe DRM
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