[PDF.86bn] Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)

Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)

[PDF.ip72] Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)

Learning Kernel Classifiers: Theory  Ralf Herbrich epub
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Learning Kernel Classifiers: Theory  Ralf Herbrich pdf file
Learning Kernel Classifiers: Theory  Ralf Herbrich audiobook
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 | #1184809 in Books |  2001-12-15 | Original language:English | PDF # 1 |  9.00 x1.10 x7.00l,1.92 | File type: PDF | 384 pages

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||3 of 12 people found the following review helpful.| Fascinating|By A Customer|An fine introduction to statistical learning theory! While the audio book format is certainly an unorthodox choice, the breathy, Jessica Rabbit-style narration turns out to be a boon when getting to grips with algorithmic stability and PAC bounds. First rate!|About the Author|Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algo...

You can specify the type of files you want, for your device.Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)   |  Ralf Herbrich. A good, fresh read, highly recommended.

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