MATLAB串接口LIBLINEAR

Question

在LIBLINEAR docs，我們有

matlab> model = train(training_label_vector, training_instance_matrix [,'liblinear_options', 'col']); 

     -training_label_vector: 
      An m by 1 vector of training labels. (type must be double) 
     -training_instance_matrix: 
      An m by n matrix of m training instances with n features. 
      It must be a sparse matrix. (type must be double) 
     -liblinear_options: 
      A string of training options in the same format as that of LIBLINEAR. 
     -col: 
      if 'col' is set, each column of training_instance_matrix is a data instance. Otherwise each row is a data instance. 

然而，即使閱讀網頁和看文檔後，我無法找出哪些選項是liblinear_options。

這是列出的地方，但我顯然想念它嗎？

Futhermore，因爲我無法找到任何地方上市liblinear_options，我堅持了以下問題：

是否train方法使用線性SVM建立一個模型？

Answer 1

Liblinear是一個線性分類器。除了支持向量機，它還包括基於邏輯迴歸的分類器。是的，正如其名稱所示，線性內核應用於SVM。

你可以檢查他們的github page爲liblinear_options。我也在這裏複製它們：

"liblinear_options:\n" 
     "-s type : set type of solver (default 1)\n" 
     "  0 -- L2-regularized logistic regression (primal)\n" 
     "  1 -- L2-regularized L2-loss support vector classification (dual)\n"   
     "  2 -- L2-regularized L2-loss support vector classification (primal)\n" 
     "  3 -- L2-regularized L1-loss support vector classification (dual)\n" 
     "  4 -- multi-class support vector classification by Crammer and Singer\n" 
     "  5 -- L1-regularized L2-loss support vector classification\n" 
     "  6 -- L1-regularized logistic regression\n" 
     "  7 -- L2-regularized logistic regression (dual)\n" 
     "-c cost : set the parameter C (default 1)\n" 
     "-e epsilon : set tolerance of termination criterion\n" 
     "  -s 0 and 2\n" 
     "    |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n" 
     "    where f is the primal function and pos/neg are # of\n" 
     "    positive/negative data (default 0.01)\n" 
     "  -s 1, 3, 4 and 7\n" 
     "    Dual maximal violation <= eps; similar to libsvm (default 0.1)\n" 
     "  -s 5 and 6\n" 
     "    |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n" 
     "    where f is the primal function (default 0.01)\n" 
     "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n" 
     "-wi weight: weights adjust the parameter C of different classes (see README for details)\n" 
     "-v n: n-fold cross validation mode\n" 
     "-q : quiet mode (no outputs)\n"