[PDF.35ie] Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts, Theory, and Methods

[PDF.xs38] Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts,  Vladimir Cherkassky, Filip M. Mulier epub
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 | #2231590 in Books |  2007-08-24 | Original language:English | PDF # 1 |  9.55 x1.32 x6.50l,2.01 | File type: PDF | 538 pages

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||3 of 4 people found the following review helpful.| good overview and in depth book on machine learning|By A. Heijs|Beside the books of vapnik and scholkopf (and thorsten joachims book on svm text mining) i think this is a valuable book if one wants to learn more about machine learning and svm. The book presents a good overview and therefore i consider the book as a good starting point if one wants to study machine learning in d| |"I think Learning From Data is a very valuable volume. I will recommend it to my graduate students." (Journal of the American Statistical Association, March 2009) |"The broad spectrum of information it offers is beneficial to many field of resea

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 statist...

You easily download any file type for your device.Learning from Data: Concepts, Theory, and Methods   |  Vladimir Cherkassky, Filip M. Mulier. I really enjoyed this book and have already told so many people about it!

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