MATLAB 火车接口(interface) LIBLINEAR

Question

在 LIBLINEAR docs ，我们有

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.

但是，即使阅读了主页并查看了文档，我也无法找到 liblinear_options 的选项。

这是否已在某处列出，但我显然错过了？

此外，由于我无法找到任何地方列出的 liblinear_options，因此我遇到了以下问题:

train 方法是否使用线性 SVM 来开发模型？

Answer 1

Liblinear是一个线性分类器。除了SVM之外，它还包括基于逻辑回归的分类器。是的，正如其名称所示，线性核应用于 SVM。

您可以查看他们的github page对于liblinear_options。我也将它们复制到这里:

"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"