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Proceedings Paper

Application of successive test feature classifier to dynamic recognition problems
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Paper Abstract

A novel successive learning algorithm is proposed for efficiently handling sequentially provided training data based on Test Feature Classifier (TFC), which is non-parametric and effective even for small data. We have proposed a novel classifier TFC utilizing prime test features (PTF) which is combination feature subsets for getting excellent performance. TFC has characteristics as follows: non-parametric learning, no mis-classification of training data. And then, in some real-world problems, the effectiveness of TFC is confirmed through way applications. However, TFC has a problem that it must be reconstructed even when any sub-set of data is changed. In the successive learning, after recognition of a set of unknown objects, they are fed into the classifier in order to obtain a modified classifier. We propose an efficient algorithm for reconstruction of PTFs, which is formalized in cases of addition and deletion of training data. In the verification experiment, using the successive learning algorithm, we can save about 70% on the total computational cost in comparison with a batch learning. We applied the proposed successive TFC to dynamic recognition problems from which the characteristic of training data changes with progress of time, and examine the characteristic by the fundamental experiments. Support Vector Machine (SVM) which is well established in algorithm and on practical application, was compared with the proposed successive TFC. And successive TFC indicated high performance compared with SVM.

Paper Details

Date Published: 6 December 2005
PDF: 10 pages
Proc. SPIE 6051, Optomechatronic Machine Vision, 60510Y (6 December 2005); doi: 10.1117/12.645706
Show Author Affiliations
Yukinobu Sakata, Hokkaido Univ. (Japan)
Shun'ichi Kaneko, Hokkaido Univ. (Japan)
Takayuki Tanaka, Hokkaido Univ. (Japan)


Published in SPIE Proceedings Vol. 6051:
Optomechatronic Machine Vision
Kazuhiko Sumi, Editor(s)

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