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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
ISBN: 0262194759, 9780262194754
Page: 644
Format: pdf
Publisher: The MIT Press

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 Series, 2002. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Bernhard Schlkopf, Alexander J. Support Vector Machines, Regularization, Optimization, and Beyond . John Shawe-Taylor, Nello Cristianini. 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) (Adaptive Computation and Machine Learning Series). Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用 Kernel. Novel indices characterizing graphical models of residues were B. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. 577, 580, Gaussian Processes for Machine Learning (MIT Press). Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Weiterführende Literatur: Abney (2008). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning).