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PsO-SVM
  • Classification:Numerical Algorithm-Artificial Intelligence - matlab
  • Development Tool:matlab
  • Sise:109 KB
  • Upload time:2011/12/30 1:05:36
  • Uploader:s604david
  • Download Statistics:
Description
I's a support vector machine to utilize Particle Swarm Optimization.




File list:
psosvm
.....\Examples
.....\........\Classification
.....\........\..............\iris1v23.mat
.....\........\..............\iris2v13.mat
.....\........\..............\iris3v12.mat
.....\........\..............\linsep.mat
.....\........\..............\nlinsep.mat
.....\........\Regression
.....\........\..........\example.mat
.....\........\..........\sinc.mat
.....\........\..........\titanium.mat
.....\Optimiser
.....\.........\Makefile
.....\.........\pr_loqo.c
.....\.........\pr_loqo.h
.....\.........\qp.c
.....\.........\qp.dll
.....\best.mat
.....\binomial.m
.....\cancer_input.mat
.....\cancer_output.mat
.....\centrefig.m
.....\Classification_SVM_SteveGunn.asv
.....\Classification_SVM_SteveGunn.m
.....\cmap.mat
.....\Contents.m
.....\fx.asv
.....\fx.m
.....\g_best.mat
.....\g_best2.mat
.....\HUIGUI-svm.asv
.....\HUIGUI-svm.m
.....\matlab.mat
.....\nobias.m
.....\pso--svm.m
.....\pso-svm.asv
.....\pso-svm.m
.....\psosvm.asv
.....\psosvm.m
.....\qp.dll
.....\quick_pso_svm.asv
.....\quick_pso_svm.m
.....\quick_pso_svm2.asv
.....\quick_pso_svm2.m
.....\quick_pso_svm3.asv
.....\quick_pso_svm3.m
.....\README
.....\README.txt
.....\Regression_SVM_SteveGunn.asv
.....\Regression_SVM_SteveGunn.m
.....\softmargin.m
.....\svc.m
.....\svcerror.m
.....\svcinfo.m
.....\svcoutput.m
.....\svcplot.m
.....\svdatanorm.m
.....\svkernel.m
.....\svr.asv
.....\svr.m
.....\svrerror.m
.....\svroutput.m
.....\svrplot.m
.....\svtol.m
.....\uiclass.m
.....\uiclass.mat
.....\uiregress.m
.....\uiregress.mat
.....\Untitled.asv
.....\Untitled.m
.....\Untitledt.asv
.....\Untitledt.m
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Related source code
[PSO-SVMface] - PSO training of SVM based face recognition using support vector machine learning of good performance, combining kernel principal component analysis and feature extraction method and apply it to the face recognition, identification of the method in the experiment showed a good performance, face recognition provides a new approach to identify in the field has passed the test.
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