关键词: 模式识别, 多类分类, 纠错输出编码, 最小二乘

Abstract: Multi-classification is the key issue in target recognition. The dichotomies so far is mostly designed for binary classification, which cannot meet the requirement of the multi-class target recognition. To solve this problem, the ECOC（Error Correcting Output Codes） is used to decompose a complex multi-classification problem into a set of binary classifications. At the same time, a decoding strategy based on least square method is proposed to fusion the dichotomies’ results. The experiments based on UCI and three kinds of different HRRPs validate that compared to the state-of-the-art dichotomies, the approach presented has better classification performance.

Key words: pattern recognition, multi-classification, Error-Correcting Output Codes（ECOC）, least square