[PDF.55dl] Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)

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

[PDF.zj20] 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 audiobook
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 | #2390072 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.

An overview of the theory and application of kernel classification methods.

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 ...

You easily download any file type for your gadget.Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)   |  Ralf Herbrich. I have read it a couple of times and even shared with my family members. Really good. Couldnt put it down.

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