%0 Journal Article
%T Positive and negative fuzzy rule system, extreme learning machine and image classification<br>正负模糊规则系统、极限学习机与图像分类
%A Wu Jun
%A Wang Shitong
%A Zhao Xin
%A <br>吴军
%A 王士同
%A 赵鑫
%J 中国图象图形学报
%D 2011
%I 
%X The positive fuzzy rules often were used only for image classification in the traditional image classification system, while the negative image classification rules were ignored in effect. Nguyen introduced the negative Fuzzy rules into the image classification, proposed a combination of positive and negative fuzzy rules to form the positive and negative fuzzy rule system, and then applied it to remote sensing image/natural image classification. Their experiments proved that their proposed method has achieved good results. However, since their method was realized using the feed forward neural network model which adjust the weights in the gradient descent, the training speed is very slow. Extreme learning machine (ELM) is a single hidden layer feed forward neural network (SLFN) learning algorithm, which has advantages such as quick learning, good generalization performance. In this paper,it proves that Extreme Learning Machine (ELM) and the positive and negative fuzzy rule system is essentially equivalent, so ELM can be naturally used for image classification. Our experimental results support this claim.
%K image classification
%K positive and negative fuzzy rules
%K extreme learning machine<br>图像分类
%K 正负模糊规则系统
%K 极限学习机
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=361297288E253233170FDE7BA2E7C52E&yid=9377ED8094509821&vid=7801E6FC5AE9020C&iid=5D311CA918CA9A03&sid=8F4C67DCFE6D499D&eid=656BC79BFC7F0F4B&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=0