基于粗糙集和支持向量机的目标识别新方法

Zhi-jun Guo, Xin He, Zhonghui Wei, G. Liang
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引用次数: 0

摘要

自动目标识别是图像应用中的一项重要任务。本文重点介绍了ATR系统的两个关键子程序:预处理和分类器设计。在预处理子程序中,提出了一种基于粗糙集(RS)的新方法,将原始样本集划分为若干子集并计算其类隶属度,从而根据类隶属度选择样本进行训练。在预处理后,引入基于粗糙集和支持向量机(IRSSVM)的迭代算法,设计了识别两类目标的分类器。实验结果表明,IRSSVM所需的训练时间更少,分类器更简单,具有更好的泛化能力和更高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new method of target recognition based on Rough Set and Support Vector Machine
Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership to be trained. After pre-treatment, an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate.
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