Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond ebook
Par jewell matthew le lundi, août 1 2016, 08:36 - Lien permanent
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
Page: 644
Format: pdf
ISBN: 0262194759, 9780262194754
Publisher: The MIT Press
Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Bernhard Schlkopf, Alexander J. "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)" "Bernhard Schlkopf, Alexander J. Weiterführende Literatur: Abney (2008). 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. Support Vector Machines, Regularization, Optimization, and Beyond . John Shawe-Taylor, Nello Cristianini. In the machine learning imagination. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. 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. Tags:Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve.