规则的多核学习支持向量回归算法

Rule_Based Multiple Kernel Learning Algorithm for Support Vector Regression

核函数的选择与数据分布信息密切相关,为了避免单一核函数选择的盲目性,提高支持向量回归机的性能,提出一种基于规则的多核支持向量回归算法。算法采用基于加法规则或基于乘法规则来获取多核,增强了核函数的非线性和多样性,进而进行多核学习。UCI数据集上的实验结果表明,与传统的支持向量回归机相比,所提算法能有效提高模型的预测精度和泛化性能,有着更为客观的优势;对比基于加法规则和基于乘法规则的多核学习算法的实验预测结果,可知两者的预测精度和模型稳定性基本相当,证实了所提算法的有效性。

For the reason that the kernel function selection is correlated closely with the distribution of experimental data, in order to improve the generalization of support vector machine model, a new rule_based multiple kernel learning algorithm for support vector regression is proposed, avoiding the blindness of a single kernel function selection. The algorithm takes the summation or multiplication of two or more than two valid kernels to get multiple kernels, enhancing the nonlinear and diversity of the kernel function. We pe^orm experiments on some datasets from the UCI Machine Learning Repository for better illustration and comparison of the proposed algorithm and the canonical support vector machine. We can find that using multiple kernels instead of a single one is helpful and more objective, which can improve the accuracy and generalization of the model at the same time. And we also believe that combing kernels in a multiplication is the same as in summation, verifying the effectively of the proposed algorithm