关键词: 模糊支持向量机, 不等距分类超平面, 特征空间, 雷达一维距离像

Abstract:

  Fuzzy support vector machines (F-SVM) algorithm is effective for pattern classification on unclassifiable sample sets condition. For the sake of enhancing the algorithm’s applicability, it was improved in this paper from two aspects. One hand, nonequidistant margin hyperplane (NM) in high dimension feature space is introduced to improve on study precision; On the other hand, effectual training sample sets in high dimension feature space are filtrated, via algorithm introduced by this paper, to reduce study time. This paper gave the theoretical derivation of improved FSVM and the filter algorithm of effectual training sample sets. The improved methods were applied to Radar Range Profile’s Recognition of two planes. Experimental results show that these methods can obtain very excellent recognition effect and reduce the algorithm study time.

Key words: fuzzy support vector machines;nonequidistant margin hyperplane;feature space;radar range profile