首页 | 本学科首页   官方微博 | 高级检索  
     

基于双支持向量回归机的增量学习算法
引用本文:郝运河,张浩峰.基于双支持向量回归机的增量学习算法[J].计算机科学,2016,43(2):230-234, 249.
作者姓名:郝运河  张浩峰
作者单位:南京理工大学计算机科学与工程学院 南京210094,南京理工大学计算机科学与工程学院 南京210094
基金项目:本文受国家自然科学基金(61101197),水下机器人技术国防科技重点实验室基金(9140C270205120C2701)资助
摘    要:提出了一种基于双支持向量回归机的增量学习算法。将获取到的新样本加入训练数据集后,该算法无需在整个新的数据集上重新训练双支持向量回归机,而是充分利用增量前的计算信息,从而大大减少了模型更新中逆矩阵的计算量,提高了算法的执行效率。在人工数据集、时间序列预测和UCI数据集上的数值实验表明,该算法快速有效。

关 键 词:双支持向量回归机  增量学习  逆矩阵  时间序列
收稿时间:2015/1/12 0:00:00
修稿时间:2015/4/13 0:00:00

Incremental Learning Algorithm Based on Twin Support Vector Regression
HAO Yun-he and ZHANG Hao-feng.Incremental Learning Algorithm Based on Twin Support Vector Regression[J].Computer Science,2016,43(2):230-234, 249.
Authors:HAO Yun-he and ZHANG Hao-feng
Affiliation:School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China and School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Abstract:This paper proposed an incremental learning algorithm based on twin support vector regression.When a new sample is added to the training set,our algorithm makes full use of old computing information instead of training all the new training set,so it greatly simplifies the calculation of inverse matrix and improves the execution efficiency.Experimental results on artificial datasets,time series and UCI datasets show that our algorithm has remarkable improvement of generalization performance with short training time.
Keywords:Twin support vector regression  Incremental learning  Inverse matrix  Time series
点击此处可从《计算机科学》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号