SVM - Revision history

Smith: /* Introduction */

2014-01-24T20:47:21Z

‎Introduction

2014-01-24T20:47:00Z

‎Introduction

2014-01-24T20:43:28Z

‎Introduction

2014-01-24T20:33:46Z

‎Results

2014-01-23T19:07:14Z

2014-01-23T18:51:44Z

‎Running an analysis

2014-01-23T00:28:43Z

2014-01-14T22:00:19Z

‎GenePattern Server Settings

2013-07-16T19:49:55Z

‎Test tab

2013-07-16T19:49:46Z

‎Train tab

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 ==Introduction==
-This component provides an interface to running the Support Vector Machines (SVM) algorithm on a [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] server.  Documentation on the GenePattern implementation of SVM is available on the at [http://www.broadinstitute.org/cancer/software/genepattern/modules GenePattern modules].
+This component provides an interface to running the Support Vector Machines (SVM) algorithm on a [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] server.  Documentation on the GenePattern implementation of SVM is available  at [http://www.broadinstitute.org/cancer/software/genepattern/modules GenePattern modules].
 SVM 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.

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 ==Introduction==
-This component provides an interface to running the Support Vector Machines (SVM) algorithm on a [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] server.
+This component provides an interface to running the Support Vector Machines (SVM) algorithm on a [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] server.  Documentation on the GenePattern implementation of SVM is available on the at [http://www.broadinstitute.org/cancer/software/genepattern/modules GenePattern modules].
 SVM 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.

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 ==Introduction==
-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.
+This component provides an interface to running the Support Vector Machines (SVM) algorithm on a [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] server.
 In geWorkbench, the SVM computation compares one or more sets of arrays marked as "Case" against sets marked "Control".

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 * '''Confidence''' - The confidence value for the assignment of the array to the class.
-References:
+==References - SVM==
 * 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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 ====Controls====
-* '''Select Microarray Set Node''' - Choose any microarray set present in the Project Folders component.
+* '''Select Microarray Set Node''' - Choose any microarray set present in the [[Workspace]].
 * '''Select Array/Phenotype Set List''' - Select a particular list of array sets available for the microarray set.
 * '''Select Microaray Set''' - Select one or more tests array sets on which to run the classifier by marking the checkboxes next to their entry.  If a set was already marked as a "test" set in the Arrays component, its entry will be automatically checked in this list.

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 The resulting classifier is placed into the [[Workspace]].
-[[Image:SVM_Project_Folder.png]]
+[[Image:SVM_in_Workspace.png]]
 ==SVM Results Viewer==

← Older revision		Revision as of 00:28, 23 January 2014
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−	The resulting classifier is placed into the ~~Project Folders component~~.	+	The resulting classifier is placed into the [[Workspace]].

	[[Image:SVM_Project_Folder.png]]		[[Image:SVM_Project_Folder.png]]

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 ==GenePattern Server Settings==
-To run GenePattern components, a GenePattern account is required.
+You can connect to any running GenePattern server to run the analysis (provided it has the required module installed).  An example configuration of the "GenePattern Server Settings" tab is shown here:
-* '''Modify''' - change any of the GenePattern connection settings.
+[[Image:GP_Server_Settings.png]]
-[[Image:SVM_GenePattern_Server_Settings.png]]
+To run GenePattern components, a GenePattern account is required.
-Pushing "Modify" brings up an editing box where any of the settings can be changed.
+* '''Protocol''' - HTTP or HTTPS, depending on the server being used.

← Older revision		Revision as of 19:49, 16 July 2013
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−	[[Image:SVM_Test_Result.png]]	+	[[Image:SVM_Test_Result.png\|{{ImageMaxWidth}}]]

← Older revision		Revision as of 19:49, 16 July 2013
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−	[[Image:SVM_Train_Result.png]]	+	[[Image:SVM_Train_Result.png\|{{ImageMaxWidth}}]]

	===Graphical results display===		===Graphical results display===