In kernel ridge regression we have seen the final solution was not sparse in the variables ® . We will now formulate a regression method that is sparse, i.e. it has the concept of support vectors that determine the solution. The thing to not

多输出的支持向量机matlab代码, 常规的svm是单输出，这个是多输出- Standard SVR formulation only considers the single-output problem. In the case of several output variables, other methods (neural networks, kernel ridge regression) must be deployed, but the good properties of

核岭回归算法 输入数据集(需要分开存放训练集和测试集) 利用4重交叉验证法调参 最后输出分类准确率(Kernel ridge regression algorithm Input data set (training set and test set need to be stored separately) Parameter adjustment by 4-fold cross validation Final output classification accuracy)