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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond by Alexander J. Smola, Bernhard Schlkopf

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Alexander J. Smola, Bernhard Schlkopf ebook
Publisher: The MIT Press
Format: pdf
Page: 644
ISBN: 0262194759, 9780262194754

Shannon CE: A mathematical theory of communication. Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. Partly this is because a number of good ideas are overly associated with them: support/non-support training datums, weighting training data, discounting data, regularization, margin and the bounding of generalization error. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , MIT Press, Cambridge, 2001. In the machine learning imagination. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Bernhard Schlkopf, Alexander J. Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Each is important even without the other: kernels are useful all over and support vector machines would be useful even if we restricted to the trivial identity kernel. John Shawe-Taylor, Nello Cristianini. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Smola, Learning with Kernels—Support Vector Machines, Regularization, Optimization and Beyond , MIT Press Series, 2002. Tags:Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve.