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文件名称:LSSVMlabv1_8_R2006a_R2009a

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    2017-09-05
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非线性分类、函数估计和密度估计等问题的有效方法。(nonlinear classification, function estimation and density estimation)
相关搜索: LS-SVMlab Toolbox

(系统自动生成,下载前可以参看下载内容)

下载文件列表

LSSVMlabv1_8_R2006a_R2009a
LSSVMlabv1_8_R2006a_R2009a\AFEm.m
LSSVMlabv1_8_R2006a_R2009a\bay_errorbar.m
LSSVMlabv1_8_R2006a_R2009a\bay_initlssvm.m
LSSVMlabv1_8_R2006a_R2009a\bay_lssvm.m
LSSVMlabv1_8_R2006a_R2009a\bay_lssvmARD.m
LSSVMlabv1_8_R2006a_R2009a\bay_modoutClass.m
LSSVMlabv1_8_R2006a_R2009a\bay_optimize.m
LSSVMlabv1_8_R2006a_R2009a\bay_rr.m
LSSVMlabv1_8_R2006a_R2009a\bitreverse32.m
LSSVMlabv1_8_R2006a_R2009a\changelssvm.m
LSSVMlabv1_8_R2006a_R2009a\cilssvm.m
LSSVMlabv1_8_R2006a_R2009a\code.m
LSSVMlabv1_8_R2006a_R2009a\codedist_bay.m
LSSVMlabv1_8_R2006a_R2009a\codedist_hamming.m
LSSVMlabv1_8_R2006a_R2009a\codedist_loss.m
LSSVMlabv1_8_R2006a_R2009a\codelssvm.m
LSSVMlabv1_8_R2006a_R2009a\code_ECOC.m
LSSVMlabv1_8_R2006a_R2009a\code_MOC.m
LSSVMlabv1_8_R2006a_R2009a\code_OneVsAll.m
LSSVMlabv1_8_R2006a_R2009a\code_OneVsOne.m
LSSVMlabv1_8_R2006a_R2009a\crossvalidate.m
LSSVMlabv1_8_R2006a_R2009a\crossvalidatelssvm.m
LSSVMlabv1_8_R2006a_R2009a\csa.m
LSSVMlabv1_8_R2006a_R2009a\democlass.m
LSSVMlabv1_8_R2006a_R2009a\democonfint.m
LSSVMlabv1_8_R2006a_R2009a\demofun.m
LSSVMlabv1_8_R2006a_R2009a\demomodel.m
LSSVMlabv1_8_R2006a_R2009a\demomulticlass.m
LSSVMlabv1_8_R2006a_R2009a\demo_fixedclass.m
LSSVMlabv1_8_R2006a_R2009a\demo_fixedsize.m
LSSVMlabv1_8_R2006a_R2009a\demo_yinyang.m
LSSVMlabv1_8_R2006a_R2009a\denoise_kpca.m
LSSVMlabv1_8_R2006a_R2009a\eign.m
LSSVMlabv1_8_R2006a_R2009a\gcrossvalidate.m
LSSVMlabv1_8_R2006a_R2009a\gcrossvalidatelssvm.m
LSSVMlabv1_8_R2006a_R2009a\gridsearch.m
LSSVMlabv1_8_R2006a_R2009a\initlssvm.m
LSSVMlabv1_8_R2006a_R2009a\kentropy.m
LSSVMlabv1_8_R2006a_R2009a\kernel_matrix.m
LSSVMlabv1_8_R2006a_R2009a\kernel_matrix2.m
LSSVMlabv1_8_R2006a_R2009a\kpca.m
LSSVMlabv1_8_R2006a_R2009a\latentlssvm.m
LSSVMlabv1_8_R2006a_R2009a\latticeseq_b2.m
LSSVMlabv1_8_R2006a_R2009a\leaveoneout.m
LSSVMlabv1_8_R2006a_R2009a\leaveoneoutlssvm.m
LSSVMlabv1_8_R2006a_R2009a\linesearch.m
LSSVMlabv1_8_R2006a_R2009a\linf.m
LSSVMlabv1_8_R2006a_R2009a\lin_kernel.m
LSSVMlabv1_8_R2006a_R2009a\lssvm.m
LSSVMlabv1_8_R2006a_R2009a\lssvmMATLAB.m
LSSVMlabv1_8_R2006a_R2009a\mae.m
LSSVMlabv1_8_R2006a_R2009a\medae.m
LSSVMlabv1_8_R2006a_R2009a\misclass.m
LSSVMlabv1_8_R2006a_R2009a\MLP_kernel.m
LSSVMlabv1_8_R2006a_R2009a\mse.m
LSSVMlabv1_8_R2006a_R2009a\plotlssvm.m
LSSVMlabv1_8_R2006a_R2009a\poly_kernel.m
LSSVMlabv1_8_R2006a_R2009a\postlssvm.m
LSSVMlabv1_8_R2006a_R2009a\predict.m
LSSVMlabv1_8_R2006a_R2009a\predlssvm.m
LSSVMlabv1_8_R2006a_R2009a\preimage_rbf.m
LSSVMlabv1_8_R2006a_R2009a\prelssvm.m
LSSVMlabv1_8_R2006a_R2009a\progress.m
LSSVMlabv1_8_R2006a_R2009a\range.m
LSSVMlabv1_8_R2006a_R2009a\RBF_kernel.m
LSSVMlabv1_8_R2006a_R2009a\rcrossvalidate.m
LSSVMlabv1_8_R2006a_R2009a\rcrossvalidatelssvm.m
LSSVMlabv1_8_R2006a_R2009a\ridgeregress.m
LSSVMlabv1_8_R2006a_R2009a\ripley.mat
LSSVMlabv1_8_R2006a_R2009a\robustlssvm.m
LSSVMlabv1_8_R2006a_R2009a\roc.m
LSSVMlabv1_8_R2006a_R2009a\rsimplex.m
LSSVMlabv1_8_R2006a_R2009a\simann.m
LSSVMlabv1_8_R2006a_R2009a\simlssvm.m
LSSVMlabv1_8_R2006a_R2009a\simplex.m
LSSVMlabv1_8_R2006a_R2009a\smootherlssvm.m
LSSVMlabv1_8_R2006a_R2009a\tbform.m
LSSVMlabv1_8_R2006a_R2009a\trainlssvm.m
LSSVMlabv1_8_R2006a_R2009a\trimmedmse.m
LSSVMlabv1_8_R2006a_R2009a\tunelssvm.m
LSSVMlabv1_8_R2006a_R2009a\weightingscheme.m
LSSVMlabv1_8_R2006a_R2009a\windowize.m
LSSVMlabv1_8_R2006a_R2009a\windowizeNARX.m

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