Support Vector Machine SVM (Support Vector Machine) it addresses the small sample, nonlinear and high dimensional pattern recognition performance of many unique advantages, and can promote the application to function approximation and other machine learning problems

LS-SVMLab v1.7(R2006a-R2009a)\AFEm.m
.............................\bay_errorbar.m
.............................\bay_initlssvm.m
.............................\bay_lssvm.m
.............................\bay_lssvmARD.m
.............................\bay_modoutClass.m
.............................\bay_optimize.m
.............................\bay_rr.m
.............................\changelssvm.m
.............................\cilssvm.m
.............................\code.m
.............................\codedist_bay.m
.............................\codedist_hamming.m
.............................\codedist_loss.m
.............................\codelssvm.m
.............................\code_ECOC.m
.............................\code_MOC.m
.............................\code_OneVsAll.m
.............................\code_OneVsOne.m
.............................\crgb_lbp.m
.............................\crossvalidate.m
.............................\crossvalidatelssvm.m
.............................\csa.m
.............................\democlass.m
.............................\democonfint.m
.............................\demofun.m
.............................\demomodel.m
.............................\demomulticlass.asv
.............................\demomulticlass.m
.............................\demo_fixedclass.m
.............................\demo_fixedsize.m
.............................\demo_yinyang.m
.............................\denoise_kpca.m
.............................\eign.m
.............................\gcrossvalidate.m
.............................\gcrossvalidatelssvm.m
.............................\getFeatures.asv
.............................\getFeatures.m
.............................\getmapping.m
.............................\gridsearch.m
.............................\initlssvm.m
.............................\kentropy.m
.............................\kernel_matrix.m
.............................\kernel_matrix2.m
.............................\kpca.m
.............................\latentlssvm.m
.............................\lbp.m
.............................\leaveoneout.m
.............................\leaveoneoutlssvm.m
.............................\linesearch.m
.............................\linf.m
.............................\lin_kernel.m
.............................\lssvm.m
.............................\lssvmMATLAB.m
.............................\medae.m
.............................\misclass.m
.............................\MLP_kernel.m
.............................\mse.m
.............................\OneVersusOne_SVM.asv
.............................\OneVersusOne_SVM.m
.............................\plotlssvm.m
.............................\poly_kernel.m
.............................\postlssvm.m
.............................\predict.m
.............................\predlssvm.m
.............................\preimage_rbf.m
.............................\prelssvm.m
.............................\progress.m
.............................\range.m
.............................\RBF_kernel.m
.............................\RBF_SVM.m
.............................\rcrossvalidate.m
.............................\rcrossvalidatelssvm.m
.............................\read.m
.............................\ReadGBFeature.m
.............................\ridgeregress.m
.............................\ripley.mat
.............................\robustlssvm.m
.............................\roc.m
.............................\rsimplex.m
.............................\simann.m
.............................\simlssvm.asv
.............................\simlssvm.m
.............................\simplex.m
.............................\smootherlssvm.m
.............................\tbform.m
.............................\trainlssvm.m
.............................\trimmedmse.m
.............................\tunelssvm.m
.............................\weightingscheme.m
.............................\windowize.m
.............................\windowizeNARX.m
LS-SVMLab v1.7(R2006a-R2009a)

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．