%0 Journal Article
%T A Constructive Kernel Covering Algorithm and Applying It to Image Recognition<br>构造性核覆盖算法在图像识别中的应用
%A ZHANG Yan-ping
%A ZHANG Ling
%A DUAN Zhen
%A ZHANG Yan-ping
%A ZHANG Ling
%A DUAN Zhen
%A ZHANG Yan-ping
%A ZHANG Ling
%A DUAN Zhen
%A <br>张燕平
%A 张铃
%A 段震
%J 中国图象图形学报
%D 2004
%I 
%X The main character of constructive neural networks is to build a network step by step during processing a given data set, during the process the construction and parameters are discovered by learning and are not presented before learning. Introducing kernel functions to non-linear transform, a support vector machine(SVM) transforms an input space into a high dimensional kernel space, then seeks the best linear classified plane in this new space. The classified function is similar to a neural network formally. A constructive kernel covering algorithm(CKCA) combines constructive learning methods of neural networks such as a covering algorithm with kernel function methods of SVM. Firstly CKCA maps the input data set into a kernel space, and then classifies the data set by using a covering algorithm in this kernel space. The CKCA method has the characteristic of low computation strong const. ructive ability and visibility there fore, it is suitable to solve the problems such as a vase high dimensional data set classification and image recognition. In this paper, CKCA is used to recognize characters of car plate which are sloped or fuzzy, and the result is satisfactory.
%K constructive
%K covering algorithm
%K kernel function
%K recognizing car plates
%K support vector machine(SVM)<br>图像识别
%K 覆盖算法
%K 神经网络
%K 体数据
%K 核函数
%K 支持向量机（SVM）
%K 字符
%K 构造性
%K 类似
%K 核空间
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=F5930713713168C8&yid=D0E58B75BFD8E51C&vid=9CF7A0430CBB2DFD&iid=708DD6B15D2464E8&sid=615D9FEA4E7369ED&eid=9A66D940867B74F7&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=14&reference_num=8