基于改进型GLVQ算法的车型分类研究

Vehicle classification research based on improved GLVQ algorithm

学习向量量化(LVQ)和泛化学习向量量化(GLVQ)算法都是采用欧氏距离作为相似性度量函数,忽视了向量各维属性的数据取值范围,从而不能区分各维属性在分类中的不同作用。针对该问题,使用一种面向特征取值范围的向量相似性度量函数,对GLVQ进行改进,提出了GLVQ-FR算法。使用视频车型分类数据进行改进型GLVQ和LVQ2.1、GLVQ、GRLVQ、GMLVQ等算法的对比实验,结果表明:GLVQ-FR算法在车型分类中具有较高的分类准确性、运算速度和真实生产环境中的可用性。

Euclidean distance is used as a vector similarity measure function in LVQ and GLVQ, which neglects the differ-ences of feature data range and affects classification accuracy of them. Aimed at this problem, a kind of vector similarity measure function with feature data range taken into account is proposed, and a new algorithm named as GLVQ-FR based on this measure function and GLVQ is put forward. Using 8 data sets of the UCI machine learning repository, the compara-tive experiments of the GLVQ-FR with the LVQ2.1, GLVQ, GRLVQ and GMLVQ algorithms are carried out, results show that the classification accuracy and computation speed of GLVQ-FR algorithm are higher than the others. The algorithm usability and high performance in real production environment is verified through the video vehicle classification data set.