利用KKT条件与类边界包向量的SVM增量学习算法

为实现对历史训练数据有选择地遗忘,并尽可能少地丢失训练样本集中的有用信息,分析了KKT条件与样本分布间的关系并得出了结论,给出了增量训练中当前训练样本集的构成.为了提高SVM增量训练速度,进一步利用训练样本集的几何结构信息对当前训练样本集进行约减,用约减后的当前训练样本集进行SVM增量训练,从而提出一种利用KKT务件与类边界包向量的快速SVM增量学习算法.实验结果表明,该算法在保持较高分类精度的同时提高了SVM增量学习速度.

Fast SVM incremental learning algorithm using KKT conditions and between-class convex hull vectors

To utilize the result of the previous training and retain the useful information in the training set effectively, the relationship between the Karush-Kuhn-Tucker (KKT) conditions of support vector machine (SVM) and the distribution of the training samples is analyzed, and the constitution of the current training sample set in the incremental learning is given. To reduce the computational cost of the SVM incremental learning, the current training sample set is reduced from the geometric point of view, and th...