├── .gitattributes ├── ConferenceSubmission.pdf ├── LICENSE ├── LR ├── LR.m ├── LR_reference.pdf ├── about glmft claiming to be linear.txt └── regression.txt ├── LS-SVM ├── LSSVM.m └── LSSVMlabv1_8_R2009b_R2011a │ ├── AFEm.m │ ├── LS-SVM.txt │ ├── LSSVMcode.m │ ├── LSSVMcode2.m │ ├── LSSVMtest.m │ ├── MLP_kernel.m │ ├── RBF_kernel.m │ ├── bay_errorbar.m │ ├── bay_initlssvm.m │ ├── bay_lssvm.m │ ├── bay_lssvmARD.m │ ├── bay_modoutClass.m │ ├── bay_optimize.m │ ├── bay_rr.m │ ├── bitreverse32.m │ ├── changelssvm.m │ ├── cilssvm.m │ ├── code.m │ ├── code_ECOC.m │ ├── code_MOC.m │ ├── code_OneVsAll.m │ ├── code_OneVsOne.m │ ├── codedist_bay.m │ ├── codedist_hamming.m │ ├── codedist_loss.m │ ├── codelssvm.m │ ├── crossvalidate.m │ ├── crossvalidatelssvm.m │ ├── csa.m │ ├── demo_fixedclass.m │ ├── demo_fixedsize.m │ ├── demo_yinyang.m │ ├── democlass.m │ ├── democonfint.m │ ├── demofun.m │ ├── demomodel.m │ ├── demomulticlass.m │ ├── denoise_kpca.m │ ├── eign.m │ ├── gcrossvalidate.m │ ├── gcrossvalidatelssvm.m │ ├── gridsearch.m │ ├── initlssvm.m │ ├── kentropy.m │ ├── kernel_matrix.m │ ├── kernel_matrix2.m │ ├── kpca.m │ ├── latentlssvm.m │ ├── latticeseq_b2.m │ ├── leaveoneout.m │ ├── leaveoneoutlssvm.m │ ├── lin_kernel.m │ ├── linesearch.m │ ├── linf.m │ ├── lssvm.m │ ├── lssvmMATLAB.m │ ├── mae.m │ ├── medae.m │ ├── misclass.m │ ├── mse.m │ ├── plotlssvm.m │ ├── poly_kernel.m │ ├── postlssvm.m │ ├── predict.m │ ├── predlssvm.m │ ├── preimage_rbf.m │ ├── prelssvm.m │ ├── progress.m │ ├── range.m │ ├── rcrossvalidate.m │ ├── rcrossvalidatelssvm.m │ ├── ridgeregress.m │ ├── robustlssvm.m │ ├── roc.m │ ├── rsimplex.m │ ├── simann.m │ ├── simlssvm.m │ ├── simplex.m │ ├── smootherlssvm.m │ ├── tbform.m │ ├── trainlssvm.m │ ├── trimmedmse.m │ ├── tunelssvm.m │ ├── weightingscheme.m │ ├── windowize.m │ └── windowizeNARX.m ├── PowerPointPresentation.pdf ├── README.md ├── feature_extraction_codes ├── cc_dwt.m ├── cc_fft.m ├── fea.m ├── fea_dwt.m ├── fea_lpn.m ├── feature_extraction.m ├── feature_extraction_99.m ├── func_FEATURE_EXTRACTION_MODULE_complete_20130907_v1.m ├── new_func_FEATURE_EXTRACTION_MODULE.m ├── st.m ├── tfrbj.m ├── tfrcw.m ├── tfrpwv.m ├── tfrstft.m ├── tfrwv.m ├── tftb_window.m └── zero_one_norm.m ├── feature_selection_codes ├── feature_selection.m └── plotfeature.m ├── key_references ├── SiulySiuly.pdf └── guide.pdf ├── libsvm-3.18 ├── COPYRIGHT ├── FAQ.html ├── Makefile ├── Makefile.win ├── README ├── heart_scale ├── java │ ├── Makefile │ ├── libsvm.jar │ ├── libsvm │ │ ├── svm.java │ │ ├── svm.m4 │ │ ├── svm_model.java │ │ ├── svm_node.java │ │ ├── svm_parameter.java │ │ ├── svm_print_interface.java │ │ └── svm_problem.java │ ├── svm_predict.java │ ├── svm_scale.java │ ├── svm_toy.java │ ├── svm_train.java │ └── test_applet.html ├── matlab │ ├── Makefile │ ├── README │ ├── acoustic │ ├── generalBT │ │ ├── fig.m │ │ ├── generalprob.m │ │ ├── generalprobZB.m │ │ ├── halfhalf.m │ │ ├── halfhalfZB.m │ │ ├── onevsalleq.m │ │ ├── onevsalliter.m │ │ └── pairwise.m │ ├── libsvmread.c │ ├── libsvmread.mexw64 │ ├── libsvmwrite.c │ ├── libsvmwrite.mexw64 │ ├── make.m │ ├── multi_svm.m │ ├── multisvm.m │ ├── svm_model_matlab.c │ ├── svm_model_matlab.h │ ├── svmpredict.c │ ├── svmpredict.mexw64 │ ├── svmtrain.c │ └── svmtrain.mexw64 ├── python │ ├── Makefile │ ├── README │ ├── svm.py │ └── svmutil.py ├── scale.mexw64 ├── svm-predict.c ├── svm-scale.c ├── svm-toy │ ├── gtk │ │ ├── Makefile │ │ ├── callbacks.cpp │ │ ├── callbacks.h │ │ ├── interface.c │ │ ├── interface.h │ │ ├── main.c │ │ └── svm-toy.glade │ ├── qt │ │ ├── Makefile │ │ └── svm-toy.cpp │ └── windows │ │ └── svm-toy.cpp ├── svm-train.c ├── svm.cpp ├── svm.def ├── svm.h ├── tools │ ├── README │ ├── checkdata.py │ ├── easy.py │ ├── grid.py │ └── subset.py └── windows │ ├── libsvm.dll │ ├── libsvmread.mexw64 │ ├── libsvmwrite.mexw64 │ ├── svm-predict.exe │ ├── svm-scale.exe │ ├── svm-toy.exe │ ├── svm-train.exe │ ├── svmpredict.mexw64 │ └── svmtrain.mexw64 ├── minFuncExamples ├── KLR.m ├── KPM │ ├── setdiag.m │ └── standardizeCols.m ├── UGM │ ├── UGM_CRF_NLL.m │ ├── UGM_CRF_PseudoNLL.m │ ├── UGM_CRF_makePotentials.m │ ├── UGM_Decode_ICM.m │ ├── UGM_Infer_LBP.m │ ├── UGM_Infer_Tree.m │ ├── UGM_MRF_NLL.m │ ├── UGM_MRF_computeSuffStat.m │ ├── UGM_MRF_makePotentials.m │ ├── UGM_Sample_Tree.m │ ├── UGM_TreeBP.m │ ├── UGM_makeEdgeFeatures.m │ ├── UGM_makeEdgeStruct.m │ ├── UGM_makeEdgeVE.m │ ├── UGM_standardizeCols.m │ ├── compiled │ │ ├── UGM_CRF_NLLC.mexa64 │ │ ├── UGM_CRF_NLLC.mexmaci64 │ │ ├── UGM_CRF_NLLC.mexw64 │ │ ├── UGM_CRF_PseudoNLLC.mexa64 │ │ ├── UGM_CRF_PseudoNLLC.mexw64 │ │ ├── UGM_CRF_makePotentialsC.mexa64 │ │ ├── UGM_CRF_makePotentialsC.mexmaci64 │ │ ├── UGM_CRF_makePotentialsC.mexw64 │ │ ├── UGM_Decode_ICMC.mexa64 │ │ ├── UGM_Decode_ICMC.mexmaci64 │ │ ├── UGM_Decode_ICMC.mexw64 │ │ ├── UGM_Infer_LBPC.mexa64 │ │ ├── UGM_Infer_LBPC.mexmaci64 │ │ ├── UGM_Infer_LBPC.mexw64 │ │ ├── UGM_LogConfigurationPotentialC.mexa64 │ │ ├── UGM_LogConfigurationPotentialC.mexmaci64 │ │ ├── UGM_LogConfigurationPotentialC.mexw64 │ │ ├── UGM_makeEdgeVEC.mexa64 │ │ ├── UGM_makeEdgeVEC.mexmaci64 │ │ └── UGM_makeEdgeVEC.mexw64 │ ├── latticeAdjMatrix.m │ └── mex │ │ ├── UGM_CRF_NLLC.c │ │ ├── UGM_CRF_PseudoNLLC.c │ │ ├── UGM_CRF_makePotentialsC.c │ │ ├── UGM_Decode_ICMC.c │ │ ├── UGM_Infer_LBPC.c │ │ ├── UGM_LogConfigurationPotentialC.c │ │ ├── UGM_common.h │ │ └── UGM_makeEdgeVEC.c ├── all │ ├── ArmijoBacktrack.m │ ├── ExtremeLoss.m │ ├── HuberLoss.m │ ├── HuberSVMLoss.m │ ├── LogisticLoss.m │ ├── MDSstress.m │ ├── MLPbinaryLoss.m │ ├── MLPregressionLoss.m │ ├── MLPregressionPredict.m │ ├── ProbitLoss.m │ ├── SSVMLoss.m │ ├── SSVMMultiLoss.m │ ├── SSVRLoss.m │ ├── SoftmaxLoss2.m │ ├── TEMKLR.m │ ├── WeightedLogisticLoss.m │ ├── WolfeLineSearch.m │ ├── X.PNG │ ├── abc.m │ ├── autoGrad.m │ ├── autoHess.m │ ├── autoHv.m │ ├── autoTensor.m │ ├── callOutput.m │ ├── conjGrad.m │ ├── dampedUpdate.m │ ├── example_minFunc.m │ ├── getColorsRGB.m │ ├── isLegal.m │ ├── ispd.m │ ├── kernelLinear.m │ ├── kernelPoly.m │ ├── kernelRBF.m │ ├── lbfgs.m │ ├── lbfgsC.c │ ├── lbfgsC.mexa64 │ ├── lbfgsC.mexglx │ ├── lbfgsC.mexmac │ ├── lbfgsC.mexmaci │ ├── lbfgsC.mexmaci64 │ ├── lbfgsC.mexw32 │ ├── lbfgsC.mexw64 │ ├── lbfgsUpdate.m │ ├── makeData.m │ ├── mchol.m │ ├── mcholC.c │ ├── mcholC.mexmaci64 │ ├── mcholC.mexw32 │ ├── mcholC.mexw64 │ ├── mcholinc.m │ ├── minFunc.m │ ├── minFunc_processInputOptions.m │ ├── multivariateT.m │ ├── multivariateTpdf.m │ ├── mylogsumexp.m │ ├── penalizedKernelL2.m │ ├── penalizedKernelL2_matrix.m │ ├── penalizedL2.m │ ├── plotClassifier.m │ ├── polyinterp.m │ ├── precondDiag.m │ ├── precondTriu.m │ ├── precondTriuDiag.m │ ├── rosenbrock.m │ ├── sampleDiscrete.m │ ├── sparsePrecisionObj.m │ ├── studentLoss.m │ └── taylorModel.m ├── crfChain │ ├── crfChain_infer.m │ ├── crfChain_initSentences.m │ ├── crfChain_initWeights.m │ ├── crfChain_loss.m │ ├── crfChain_makePotentials.m │ └── crfChain_splitWeights.m ├── lossFuncs │ ├── ExtremeLoss.m │ ├── HuberLoss.m │ ├── HuberSVMLoss.m │ ├── LogisticLoss.m │ ├── MDSstress.m │ ├── MLPbinaryLoss.m │ ├── MLPregressionLoss.m │ ├── MLPregressionPredict.m │ ├── ProbitLoss.m │ ├── SSVMLoss.m │ ├── SSVMMultiLoss.m │ ├── SSVRLoss.m │ ├── SoftmaxLoss2.m │ ├── WeightedLogisticLoss.m │ ├── kernelLinear.m │ ├── kernelPoly.m │ ├── kernelRBF.m │ ├── multivariateT.m │ ├── multivariateTpdf.m │ ├── penalizedKernelL2.m │ ├── penalizedKernelL2_matrix.m │ ├── penalizedL2.m │ ├── sparsePrecisionObj.m │ └── studentLoss.m ├── mexAll.m ├── minFunc │ ├── ArmijoBacktrack.m │ ├── WolfeLineSearch.m │ ├── autoGrad.m │ ├── autoHess.m │ ├── autoHv.m │ ├── autoTensor.m │ ├── callOutput.m │ ├── conjGrad.m │ ├── dampedUpdate.m │ ├── example_minFunc.m │ ├── isLegal.m │ ├── lbfgs.m │ ├── lbfgsC.c │ ├── lbfgsC.mexa64 │ ├── lbfgsC.mexglx │ ├── lbfgsC.mexmac │ ├── lbfgsC.mexmaci │ ├── lbfgsC.mexmaci64 │ ├── lbfgsC.mexw32 │ 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