计算机应用与软件2016，Vol.33Issue(9)：269-272,4.DOI:10.3969/j.issn.1000-386x.2016.09.063
一种基于极限学习机的在线负增量算法
AN ONLINE NEGATIVE INCREMENTAL ALGORITHM BASED ON EXTREME LEARNING MACHINE
摘要
Abstract
After weeding out the dirty data that affecting the performance of single hidden layer feedforward network,traditional extreme learning machine has the need to train the entire networks.However,this will increase a lot of extra training time.In light of this issue,the paper proposes an online negative incremental algorithm based on traditional extreme learning machine algorithm:after the “dirty training sample”being eliminated,there has no need to train the whole networks once again,but only need to accomplish the network update by updating output weights matrix on the basis of original.The complexity analysis of the algorithm and the result of simulation experiment show that the proposed algorithm has higher execution speed.关键词
极限学习机/负增量算法/算法复杂性/仿真实验Key words
Extreme learning machine/Negative incremental algorithm/Algorithm’s complexity/Simulation experiment分类
信息技术与安全科学引用本文复制引用
谢林森,任婷婷,卢诚波..一种基于极限学习机的在线负增量算法[J].计算机应用与软件,2016,33(9):269-272,4.基金项目
国家自然科学基金面上项目（11171137）；浙江省自然科学基金项目（LY13A010008）。 （）
