【作者】 张健沛； 李忠伟； 杨静；

【机构】 哈尔滨工程大学计算机科学与技术学院；

【摘要】 <正>Support Vector Machine(SVM) can be trained and to learn incrementally when dealing with large-scale problems with batch training model,but the disadvantage is that its computation and storage requirements increase rapidly with the number of training vector.To overcome it,parallel training methods have been proposed by splitting the problem into smaller subsets and training a network.A parallel training algorithm with multiple SVM classifiers to incremental learning is proposed in this paper,in which multiple SVM classifiers are applied to batch model. Each classifier is trained by updating support vectors set with that of other classifiers to avoid the problem that the training results are subject to numbers of batches and state of data distribution.The experiment results on realworld text dataset show that the parallel training algorithm with multiple SVM classifiers has more satisfying accuracy compared with batch training algorithm with a single SVM.更多还原

【Abstract】 Support Vector Machine(SVM) can be trained and to learn incrementally when dealing with large-scale problems with batch training model,but the disadvantage is that its computation and storage requirements increase rapidly with the number of training vector.To overcome it,parallel training methods have been proposed by splitting the problem into smaller subsets and training a network.A parallel training algorithm with multiple SVM classifiers to incremental learning is proposed in this paper,in which multiple SVM classifiers are applied to batch model. Each classifier is trained by updating support vectors set with that of other classifiers to avoid the problem that the training results are subject to numbers of batches and state of data distribution.The experiment results on realworld text dataset show that the parallel training algorithm with multiple SVM classifiers has more satisfying accuracy compared with batch training algorithm with a single SVM.更多还原