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Learning with Kernels: Support Vector Machines,

Learning with Kernels: Support Vector Machines,

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. 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.pdf
ISBN: 0262194759,9780262194754 | 644 pages | 17 Mb


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



Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Alexander J. Smola, Bernhard Schlkopf
Publisher: The MIT Press




Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用 Kernel. "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)" "Bernhard Schlkopf, Alexander J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Publisher The MIT Press Author(s) Alexander J. Smola, Learning with Kernels—Support Vector Machines, Regularization, Optimization and Beyond , MIT Press Series, 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Weiterführende Literatur: Abney (2008). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , MIT Press, Cambridge, 2001. Novel indices characterizing graphical models of residues were B. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning series) - The MIT Press - ecs4.com. Shannon CE: A mathematical theory of communication. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning Series). Support Vector Machines, Regularization, Optimization, and Beyond .