侧边栏
zh:notes:ml2s_svm
这是本文档旧的修订版！
目录
ML$^2$S-SVM: Multi-Label Least-Squares Support Vector Machine Classifiers
Requirements
Parameter Turing & Model Running with ML2S-SVM
The parameters are tuned with 10-fold cross validation, and then Train and test on the specific data.
Dataset: scene
load scene [gamma_best, mu_best, p_best, loss_best] = GridML2SSVM(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Dataset: emotions
load emotions [gamma_best, mu_best, p_best, loss_best] = GridML2SSVM(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Dataset: yeast
load yeast [gamma_best, mu_best, p_best, loss_best] = GridML2SSVM(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Dataset: bibtex
load bibtex [gamma_best, mu_best, p_best, loss_best] = GridML2SSVM(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Parameter Turing & Model Running with MTLS-SVM
The parameters are tuned with 10-fold cross validation, and then train and test on the specific data.
Dataset: scene
load scene [gamma_best, lambda_best, p_best, loss_best] = GridMTLSSVC(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Dataset: emotions
load emotions [gamma_best, lambda_best, p_best, loss_best] = GridMTLSSVC(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Dataset: yeast
load yeast [gamma_best, lambda_best, p_best, loss_best] = GridMTLSSVC(train_inst, train_lbl, 10, 0, 0, 0, inf); [alpha, b] = ML2SVMTrain(train_inst, train_lbl, gamma_best, mu_best, p_best); [predict_lbl]= ML2SSVMPredict(test_inst, train_inst, train_lbl, alpha, b, mu_best, p_best);
Dataset: bibtex
zh/notes/ml2s_svm.1578290711.txt.gz · 最后更改: 2020/01/06 14:05 由 pzczxs
评论