LSSVMlab_v17_R2009b_R2010b.zip

时间:2014-01-24 09:22:24
【文件属性】:

文件名称:LSSVMlab_v17_R2009b_R2010b.zip

文件大小:120KB

文件格式:ZIP

更新时间:2014-01-24 09:22:24

LSSVMlab_v17_R2009b_R2010b.zip

We have added new functions to the toolbox and updated some of the existing commands with respect to the previous version v1.6. Because many readers are familiar with the layout of version 1.5 and version 1.6, we have tried to change it as little as possible. Here is a summary of the main changes: • The major difference with the previous version is the optimization routine used to find the minimum of the cross-validation score function. The tuning procedure consists out of two steps: 1) Coupled Simulated Annealing determines suitable tuning parameters and 2) a simplex method uses these previous values as starting values in order to perform a finetuning of the parameters. The major advantage is speed. The number of function evaluations needed to find optimal parameters reduces from ±200 in v1.6 to 50 in this version. • The construction of bias-corrected approximate 100(1 − α)% pointwise/simulataneous confidence and prediction intervals have been added to this version. • Some bug-fixes are performed in the function roc. The class do not


【文件预览】:
LS-SVMLab v1.7 (R2009b-R2010b)
----codedist_hamming.m(756B)
----codedist_loss.m(2KB)
----denoise_kpca.m(4KB)
----RBF_kernel.m(1KB)
----bay_rr.m(4KB)
----bay_initlssvm.m(2KB)
----trimmedmse.m(2KB)
----roc.m(7KB)
----simplex.m(10KB)
----trainlssvm.m(9KB)
----gridsearch.m(7KB)
----eign.m(4KB)
----code_OneVsOne.m(579B)
----preimage_rbf.m(4KB)
----demomodel.m(5KB)
----simann.m(5KB)
----bay_optimize.m(6KB)
----predict.m(3KB)
----weightingscheme.m(931B)
----demomulticlass.m(2KB)
----leaveoneoutlssvm.m(2KB)
----cilssvm.m(4KB)
----leaveoneout.m(4KB)
----csa.m(3KB)
----smootherlssvm.m(1KB)
----poly_kernel.m(623B)
----demo_yinyang.m(3KB)
----lin_kernel.m(529B)
----ripley.mat(4KB)
----bay_errorbar.m(6KB)
----plotlssvm.m(10KB)
----codelssvm.m(4KB)
----prelssvm.m(6KB)
----AFEm.m(3KB)
----lssvm.m(2KB)
----linf.m(313B)
----codedist_bay.m(2KB)
----code.m(4KB)
----predlssvm.m(5KB)
----mse.m(285B)
----ridgeregress.m(1KB)
----crossvalidate.m(5KB)
----gcrossvalidate.m(3KB)
----medae.m(306B)
----bay_lssvmARD.m(8KB)
----windowize.m(2KB)
----MLP_kernel.m(608B)
----code_MOC.m(550B)
----simlssvm.m(6KB)
----crossvalidatelssvm.m(4KB)
----windowizeNARX.m(2KB)
----democlass.m(3KB)
----robustlssvm.m(3KB)
----misclass.m(688B)
----kernel_matrix.m(3KB)
----progress.m(1KB)
----gcrossvalidatelssvm.m(2KB)
----democonfint.m(2KB)
----kentropy.m(2KB)
----tunelssvm.m(22KB)
----range.m(173B)
----code_OneVsAll.m(364B)
----lssvmMATLAB.m(2KB)
----rsimplex.m(10KB)
----changelssvm.m(5KB)
----demo_fixedsize.m(3KB)
----kpca.m(6KB)
----bay_lssvm.m(10KB)
----latentlssvm.m(2KB)
----bay_modoutClass.m(9KB)
----linesearch.m(4KB)
----demo_fixedclass.m(2KB)
----initlssvm.m(3KB)
----demofun.m(4KB)
----rcrossvalidatelssvm.m(4KB)
----code_ECOC.m(5KB)
----postlssvm.m(5KB)
----tbform.m(2KB)
----rcrossvalidate.m(6KB)
----kernel_matrix2.m(583B)

网友评论

  • 我现在做EDR用KPCA就是用的这个工具箱,很好