[PDF.28wf] Learning from Data: Concepts, Theory, and Methods
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Learning from Data: Concepts, Theory, and Methods
Vladimir Cherkassky, Filip M. Mulier
[PDF.bo52] Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Vladimir Cherkassky, Filip M. Mulier epub Learning from Data: Concepts, Vladimir Cherkassky, Filip M. Mulier pdf download Learning from Data: Concepts, Vladimir Cherkassky, Filip M. Mulier pdf file Learning from Data: Concepts, Vladimir Cherkassky, Filip M. Mulier audiobook Learning from Data: Concepts, Vladimir Cherkassky, Filip M. Mulier book review Learning from Data: Concepts, Vladimir Cherkassky, Filip M. Mulier summary
| #1147538 in Books | 2007-08-24 | Original language:English | PDF # 1 | 9.55 x1.32 x6.50l,2.01 | File type: PDF | 538 pages||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 can specify the type of files you want, for your device.Learning from Data: Concepts, Theory, and Methods | Vladimir Cherkassky, Filip M. Mulier. Just read it with an open mind because none of us really know.
