Mon premier blog

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

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning Series). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001. Weiterführende Literatur: Abney (2008). Support Vector Machines, Regularization, Optimization, and Beyond . Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were B. Shannon CE: A mathematical theory of communication. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001.