期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2006
卷号：XXXVI Part 5
出版社：Copernicus Publications
摘要：Fundamental characteristics of successive lerarning by Test Feature Classifier(TFC), which is non-parametric and effective with small data, are examined. In the learning, a new set of training objects, they are fed into the classifier in order to obtain a modified classifier. We propose an efficient algorithm for reconstruction of prime test features, which are combinaton feature subsets for getting the excellent performance. We apply the proposed successive TFC to dynamic recognition problems where the traning data increase successively and also characteristic of the data change with progress of time, and examine the characteristic by the experiments which used the real world data and a set of simulated data
关键词：classification; test feature classifier; successive learning; dynamic recognition problem
