Learning from Data
Concepts, Theory, and Methods
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Beschreibung
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.
Autor*in
Vladimir Cherkassky
Themen in »Learning from Data«
Data Mining Data Mining Statistics Electrical & Electronics Engineering Elektrotechnik u. Elektronik Intelligent Systems & Agents Intelligente Systeme u. Agenten Maschinelles Lernen Methoden der Daten- u. Stichprobenerhebung Neuronales Netz Signal Statistics Statistik Survey Research Methods & Sampling Unscharfe Menge
Stimmen zu »Learning from Data«
"I think Learning From Data is a very valuable volume. Iwill recommend it to my graduate students." (Journal of theAmerican Statistical Association, March 2009)
"The broad spectrum of information it offers is beneficial tomany field of research. The selection of topics is good, and Ibelieve that many researchers and practioners will find this bookuseful." (Technometrics, May 2008)
"The authors have succeeded in summarizing some of the recenttrends and future challenges in different learning methods,including enabling technologies and some interesting practicalapplications." (Computing Reviews, May 22, 2008)
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