Application of Adaptive Committee Classifiers in On-Line Character Recognition

Matti Aksela, Jorma Laaksonen, Erkki Oja, Jari Kangas

Abstract: There are two main approaches to classifier adaptation. A single adaptive classifier can be used, or an adaptive committee of classifiers whose members can be either adaptive or non-adaptive. We have experimented with some approaches to adaptive committee operations, including the Dynamically Expanding Context (DEC) and the Modified Current-Best-Learning (MCBL) approaches In the experiments of this paper the feasibility of using an adaptive committee classifier is explored and tested with on-line character recognition. The results clearly show that the use of adaptive committees can improve on the recognition results, both in comparison to the individual member classifiers and the non-adaptive reference committee.