[PDF.22hp] The Minimum Description Length Principle (Adaptive Computation and Machine Learning series)

The Minimum Description Length Principle (Adaptive Computation and Machine Learning series)

[PDF.zy71] The Minimum Description Length Principle (Adaptive Computation and Machine Learning series)

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 | #199768 in Books |  2007-03-23 | Original language:English | PDF # 1 |  9.00 x1.50 x7.00l,2.54 | File type: PDF | 736 pages

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||4 of 19 people found the following review helpful.| VERY heavy on theory & math|By G. Caprio|This book is provides a good overview to the theory behind MDL. Not for the faint of heart, however.|19 of 20 people found the following review helpful.| An excellent, deep summary of MDL theory.|By Someone|This book provides an excellent, in-depth summary of current MDL theory. It is|About the Author|Peter D. Grünwald is a researcher at CWI, the National Research Institute for Mathematics and Computer Science, Amsterdam, the Netherlands. He is also affiliated with EURANDOM, the European Research Institute for the Study of Stochastic Phe

The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can ...

You can specify the type of files you want, for your device.The Minimum Description Length Principle (Adaptive Computation and Machine Learning series)   |  Peter D. Grünwald. A good, fresh read, highly recommended.

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