快速原空间孪生支持向量回归算法

Fast Twin Support Vector Regression Algorithm in Primal Space

孪生支持向量回归(TSVR)通过快速优化一对较小规模的支持向量机问题获得回归函数.文中提出在原始输入空间中采用Newton法直接优化TSVR的目标函数,从而有效克服TSVR通过对偶二次规划问题求得近似最优解导致性能上的损失.数值模拟实验表明该方法不仅能提高TSVR的性能,并且可降低学习时间.

Twin support vector regression （TSVR） efficiently determines its objective regression function by optimizing a pair of smaller sized SVM-type problems. The objective functions of TSVR in the primal space are directly optimized by introducing the well-known Newton algorithm. This method effectively overcomes the shortcoming of TSVR that its regressor is approximated by the dual quadratic programming problems. Numerical studies show that the proposed method provides good performance and obtains less learning time compared with TSVR.