%0 Journal Article
%T Image classification based on transductive support vector machines<br>基于直推式支持向量机的图像分类算法
%A SHEN Xin-yu
%A XU Hong-li
%A GUAN Teng-fei
%A <br>沈新宇
%A 许宏丽
%A 官腾飞
%J 计算机应用
%D 2007
%I 
%X Transductive Support Vector Machines (TSVM) take advantage of the test sets as well as the train sets and inherit most properties of inductive SVMs. They are more efficient than inductive SVMs, especially for very small training sets and large test sets. But they still have disadvantages, such as high time complexity and the requirement of "num ". The improved algorithm substantially reduces the time complexity with little influence on the performance.
%K Support Vector Machine (SVM)
%K transductive learning
%K image classification<br>支持向量机
%K 直推式学习
%K 图像分类
%K 直推式
%K 支持向量机算法
%K 图像
%K 分类算法
%K support
%K vector
%K machines
%K based
%K classification
%K 算法时间复杂度
%K 改进
%K 比例
%K 负例
%K 设置
%K 比较
%K 效果
%K 测试集
%K 训练集
%K 结合
%K 影响
%K 分类器
%K 样本
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD27430512D8F149D25BDF078C&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=B31275AF3241DB2D&sid=5670EE5C13D54BD2&eid=AA2E209AD442C5CC&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=6