基于超球的支持向量机增量学习算法

支持向量机方法已经成功地应用于解决分类和回归问题,但是在训练支持向量机时需要求解二次规划问题,使得支持向量机的训练时间过长,训练样本量越大,这个缺陷越明显.将超球方法与回归支持向量机相结合,提出一种增量学习的新方法.该方法使用两个同心超球缩减训练集,以达到提高训练速度的目的.通过分析表明,这种新的增量学习方法较普通支持向量机训练方法有较低的计算复杂度.实验结果表明,该算法可以在不降低预测准确性的同时减少大量建模时间.

Incremental Learning of Support Vector Machine Based on Hyperspheres

Support vector machine (SVM) has been successfully applied to solving classification and regression problems. However,to solve the quadratic programming problem is needed for SVM training process,and the more the number of training samples,the longer the training process. A novel incremental learning method was therefore proposed combining the hypershpere approach with regressional SVM to reduce the number of training samples by using two concentric hypershperes,thus shortening the training time. Analysis r...