%0 Journal Article
%T 基于在线连续极限学习机的图像分类改进算法<br>A modified algorithm for image classification based on online sequential extreme learning machine
%A 陈建原
%A 何建农
%J 福州大学学报(自然科学版)
%D 2015
%R 10.7631/issn.1000-2243.2015.02.0176
%X 结合LBP算子提取图像的局部纹理特征，在分类阶段根据优化解进行矩阵逆的区别计算并加入正则因子，最后结合在线学习方法，提出准确在线连续极限学习机的图像分类改进算法. 实验结果表明，改进算法在图像分类方面比传统的极限学习机有更快的学习速度，更好的泛化性能.<br>Algorithm extract textrual features from subregion of image by using LBP first. Secondly，different manipulation of inverse of matrix is made according to the optimal solution and using regularizetion factor at the same time. Motivated by online learning method，accurate online sequential extreme learning machine(AOS-ELM) is designed. Experimental results demonstrate that the AOS-ELM can learn faster and achieve better performance than traditional ELM
%K 图像分类 极限学习机 在线学习 神经网络 局部二值模式&lt
%K br&gt
%K image classification extreme learning machine online learning neural network local binary pattern
%U http://xbzrb.fzu.edu.cn/ch/reader/view_abstract.aspx?file_no=201502006&flag=1