如何使用libsvm库(Matlab)实现1对1多类分类？

如何使用libsvm实现一对一多类分类？请帮我解决这个问题.

我还从这个答案中读到了一对一的方法...... 使用Matlab进行交叉验证的多类SVM的完整示例[关闭]

我的测试数据:功能和最后一列是标签

D = [

1           1          1           1             1
1           1          1           9             1
1           1          1           1             1
11          11         11          11            2
11          11         11          11            2
11          11         11          11            2
30          30         30          30            3
30          30         30          30            3
30          30         30          30            3
60          60         60          60            4
60          60         60          60            4
60          60         60          60            4
];

我的测试数据是

inputTest = [
    1           1           1           1             
    11          11          11          10            
    29          29          29          30            
    60          60          60          60            
];

rzo.. 5

LIBSVM提供了一个Matlab接口.在包中,有一个很好README的如何通过Matlab使用这个接口.

用法是:

matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);

使用以下参数:

    -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 can be dense or sparse (type must be double).
    -libsvm_options:
        A string of training options in the same format as that of LIBSVM.

但是,由12个示例组成的训练数据不足以构建良好的SVM分类器.您应该获得更多培训和测试过程的示例.

LIBSVM提供了一个Matlab接口.在包中,有一个很好README的如何通过Matlab使用这个接口.

用法是:

matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);

使用以下参数:

    -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 can be dense or sparse (type must be double).
    -libsvm_options:
        A string of training options in the same format as that of LIBSVM.

但是,由12个示例组成的训练数据不足以构建良好的SVM分类器.您应该获得更多培训和测试过程的示例.