Learning from Data: Concepts, Theory, and Methods

by Vladimir Cherkassky, Filip M. Mulier

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.

Genres: Mathematics, Reference

560 pages, Hardcover

First published March 25, 1998