Question

在LIBLINEAR docs中，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，即使在阅读完主页并查看文档后，我也无法找到liblinear_options的选项。

这是否列在某处，但我显然错过了它？

此外，由于我无法在任何地方找到liblinear_options，因此我坚持以下问题：

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，它还包括基于逻辑回归的分类器。是的，正如其名称所示，线性内核应用于SVM。

您可以检查liblinear_options "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"的{{1}}。我也在这里复制了它们：

{{1}}

Answer 2

自发布以来可能有一些新进展。在 matlab 提示符下运行 train 将为您提供所有选项。至少在 R2020b 上使用我刚下载的 liblinear 版本。

@stats

MATLAB训练界面LIBLINEAR

2 个答案: