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

Bernhard Schlkopf, Alexander J. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001. Tags:Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve. Novel indices characterizing graphical models of residues were B. Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. In the machine learning imagination. "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)" "Bernhard Schlkopf, Alexander J. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. John Shawe-Taylor, Nello Cristianini. Shannon CE: A mathematical theory of communication. 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. 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.