SVM

Revision as of 18:36, 7 March 2011 by Smith (talk | contribs)

The Support Vector Machines (SVM) module implements the support vector machines algorithm. It is a supervised classification method that computes a maximal separating hyperplane between the expression vectors of different classes or phenotypes. Given microarray data with n markers per sample, SVM outputs a hyperplane,W, which can be thought of as a vector with n components each corresponding to the expression of a particular marker. Loosely speaking, assuming that the expression values of each marker have similar ranges, the absolute magnitude of each element in W determines its importance in classifying a sample. 


SVM Parameters tab.png


SVM GenePattern Server Settings.png


SVM Server Settings.png


SVM Help tab.png


SVM Arrays Setup.png


SVM Running Classifier train.png


SVM Running Classifier test.png


SVM Project Folder.png


SVM Train Result.png


SVM Test Result.png


References:

  • R. Rifkin, S. Mukherjee, P. Tamayo, S. Ramaswamy, C-H Yeang, M. Angelo, M. Reich, T. Poggio, E.S. Lander, T.R. Golub, J.P. Mesirov, An Analytical Method for Multiclass Molecular Cancer Classification, SIAM Review, 45:4, (2003).
  • T. Evgeniou, M. Pontil, T. Poggio, Regularization networks and support vector machines, Adv. Comput. Math., 13 (2000), pp. 1-50.
  • V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
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