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Software Related Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Posted on 2010-03-15




Name:Software Related Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
ASIN/ISBN:0262194759
Language:English
File size:36.2 Mb
Publisher: The MIT Press; 1st edition (December 15, 2001)
ISBN: 0262194759
File Type: PDF
File Size: 36,2 Mb
Pages: 644 pages
   Software Related Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) by Bernhard Schlkopf, Alexander J. Smola



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
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Programming Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Repost)

Programming Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Repost)

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