关键词: 车型分类, 学习向量量化, 相似性度量, 模式识别

Abstract: Euclidean distance is used as a vector similarity measure function in LVQ and GLVQ, which neglects the differences 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 comparative 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.

Key words: vehicle classification, Learning Vector Quantization（LVQ）, similarity metric, pattern recognition