计算机技术与发展Issue(2)：112-114,118,4.DOI:10.3969/j.jssn.1673-629X.2013.02.028
一种自适应的半监督图像分类算法
An Adaptive Semi-supervised Image Classification Algorithm
摘要
Abstract
Learning with local and global consistency algorithm with a certain number of parameters, and the parameter selection of delta is sensitive about the number of iterations of the algorithm iterative process and classification results , usually by manually set of experi-ments, this approach is relatively time-consuming. In order to solve the problem,improve the classification efficiency of algorithm, this paper applies the algorithm to image classification and proposes an adaptive parameter setting method, determining the best range of the parameter delta. The experimental results show that, this paper determines the values of the parameter range can make highest classifica-tion correct rate and shortest iterative time of the algorithm;therefore this method can effectively improve the classification efficiency of algorithm.关键词
图像分类/半监督/半监督学习Key words
image classification/Semi-supervised/Semi-supervised learning分类
信息技术与安全科学引用本文复制引用
李亚娥,汪西莉..一种自适应的半监督图像分类算法[J].计算机技术与发展,2013,(2):112-114,118,4.基金项目
国家自然科学基金资助项目(41171338) （41171338）
