关键词: 增量学习, 最近邻算法, 匹配度

Abstract: In order to solve the classifier problem of learning new sample knowledge, an incremental learning algorithm based on distance is proposed. The algorithm is based on the nearest neighbor algorithm to increase the function of incremental learning. Calculating the matching degree between new input sample and model samples, the algorithm finds the best and second best matching degrees and compares them with the threshold. The comparative results decide whether it increases samples amount or adds a new category to realize incremental learning. The algorithm is applied to the UCI standard database and experiment of vehicle type recognition. The experimental results prove the algorithm’s efficient. Also, more experiments are conducted to analyze and verify how standard sample amount and matching degree threshold affect the final results.

Key words: incremental learning, nearest neighbor algorithm, matching degree