MATLAB多分类，使用libsvm和linear的详解和区别

MATLAB多分类，使用libsvm和linear的区别

最近还是在做多分类的问题，之前使用的是libsvm进行多分类，主要使用的是1V1和1Vrsst，具体可参见这篇博文。这两天看到liblinear这个库，针对多分类问题有很好的改进。这里随笔记下：

liblinear链接，liblinear相比于libsvm最大的优点是快，二者针对多分类的区别在官网也有详细的解释：

For some multi-class data, the difference may be significant. The reason is that LIBSVM uses the 1-vs-1 strategy, while LIBLINEAR uses 1-vs-the rest.

意思就是LIBSVM 实现多分类的策略是1v1，而LIBLINEAR 用的是1-vs-the rest,

另外在此再解释一下LIBLINEAR 的调用：

1·数据结构和LIBSVM 保持一致，

2·但是调用参数有些变化

Usage: train [options] training_set_file [model_file]

options:

-s type : set type of solver (default 1)

for multi-class classification

0 – L2-regularized logistic regression (primal)

1 – L2-regularized L2-loss support vector classification (dual)

2 – L2-regularized L2-loss support vector classification (primal)

3 – L2-regularized L1-loss support vector classification (dual)

4 – support vector classification by Crammer and Singer

5 – L1-regularized L2-loss support vector classification

6 – L1-regularized logistic regression

7 – L2-regularized logistic regression (dual)

for regression

11 – L2-regularized L2-loss support vector regression (primal)

12 – L2-regularized L2-loss support vector regression (dual)

13 – L2-regularized L1-loss support vector regression (dual)

这里指出，liblinear的-s 参数能够制定不同的多分类策略，这里的4类型使用的 Crammer and Singer是一种较好的多分类方法，在参考文献:K. Crammer and Y. Singer, “On the learnability and design of output

codes for multiclass problems,” Comput. Learing Theory, pp. 35–46,

2000.这篇文章中提出。