[PDF.54qj] Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

[PDF.el10] Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

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 | #409197 in Books |  The MIT Press |  2001-12-15 | Original language:English | PDF # 1 |  10.00 x1.06 x8.00l,3.28 | File type: PDF | 644 pages

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||0 of 0 people found the following review helpful.| Learning from experts|By doc worker2|This book wanted to be the comprehensive discussion about SVM and related topics, but reality kicked in, and many topics had to be left out. What is here, though, is a thorough coverage of the most important areas. The authors do note at the beginning of each chapter which sections are essential, and which can be omitted at first reading. If| ||Interesting and original. Learning with Kernels will make a fine textbook on this subject. (Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison)

|This splendid book fills the need for a comprehensive treatment of ke

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replaci...

You easily download any file type for your gadget.Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)   |  Bernhard Schlkopf, Alexander J. Smola. I was recommended this book by a dear friend of mine.

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