%0 Journal Article
%T KNN classification algorithm based on rule of weak learning on small sample sets<br>基于小样本集弱学习规则的KNN分类算法
%A LENG Ming-wei
%A CHEN Xiao-yun
%A TAN Guo-lv
%A <br>冷明伟
%A 陈晓云
%A 谭国律
%J 计算机应用研究
%D 2011
%I 
%X KNN and its improved algorithms identify the class labels of the unlabel datasets by using the label datasets , if the data objects in are very little, and this will influence the accuracy of classification. Improving the accuracy of classification is the goal of KNN classification algorithm based on the rule of weak learning on small sample sets, which learns the label information of objects in based on firstly, selects some data objects in and labels them by using the learned label information, and then adds those data objects into , finally labels the objects in based on the expanded label datasets . The accuracy of the presented method is demonstrated with standard datasets, and obtains a satisfying result.
%K machine learning
%K K-nearest neighbor classification
%K small sample sets
%K label data
%K weak learning rule<br>机器学习
%K K最近邻分类
%K 小样本集
%K 标签数据
%K 弱学习规则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=26E35499961EB256091154A4BAFB01F7&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=38B194292C032A66&sid=BD77137A0285B6FF&eid=3D9E2C3DB640307A&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=7