改进的稀疏孪生支持向量回归算法

相比传统支持向量机，尽管孪生支持向量机具有较快的计算速度，然而不具备结构风险最小化和稀疏性，易产生过拟合现象。针对这一问题，提出了一种具有稀疏性的改进的孪生支持向量回归算法。通过在目标函数中加入正则项将结构风险最小化原则引入到孪生支持向量回归算法中，改善了算法的回归性能|同时选择训练样本的一个子集代替全部的训练样本，使核函数由方阵转变成矩形阵，从而使算法具有稀疏性，有效减少运算时间。仿真结果证明了该算法的有效性。

Improved sparse twin support vector regression algorithm

Compared with the traditional support vector machine, the twin support vector machine owns faster calculation speed. However, the twin support vector machine is easy to produce over-fitting and has low computational efficiency, which does not have structure risk minimization characteristics and parsimoniousness. In order to solve this problem, an improved sparse twin support vector regression (ISTSVR) algorithm is proposed. The twin support vector regression algorithm combined with structure risk minimization principle improves the regression performance of the algorithm by adding a canonical term in the objective function. At the same time, a subset of train samples is selected to take place of the whole train sample, which makes the kernel function from square into a rectangular matrix, thus making the algorithm own sparseness and effectively reduces the computation time. Simulation results are provided to validate the effectiveness of the proposed algorithm.