Improved adaptive pruning algorithm for least squares support vector regression
Improved adaptive pruning algorithm for least squares support vector regression
复制标题
改进的最小二乘支持向量回归自适应剪枝算法
DOI：
10.1109/jsee.2012.00055
复制
发表时间：
2012-07
影响因子：
2.1
通讯作者：
San, Ye
中科院分区：
文献类型：
--
作者：
Gao, Runpeng;San, Ye
最小二乘支持向量机(LS-SVRM)由于解的非稀疏性，导致预测速度慢，限制了其应用。已有的LS-SVRM自适应剪枝算法存在训练速度慢、泛化性能不理想等缺点，特别是对于大规模问题。在此基础上提出了一种改进算法。为了加快训练速度，采用剪枝后的数据点和快速留一误差对递减学习后得到的临时模型进行验证。为了提高泛化性能，采用了包含所有训练数据点产生的整体约束的终止条件下的目标函数和三种剪枝策略。在6个基准数据集上测试了该算法的有效性。稀疏LS-SVRM模型具有更快的训练速度和更好的泛化性能。
As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the existing adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satisfactory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which involves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generalization performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.