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



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




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


In the machine learning imagination. Novel indices characterizing graphical models of residues were B. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. 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. 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. 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. Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Publisher The MIT Press Author(s) Alexander J. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond.