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.
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Aksela, M., Laaksonen, J., Oja, E., Kangas, J. (2001). Application of adaptive committee classifiers in on-line character recognition. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_28
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DOI: https://doi.org/10.1007/3-540-44732-6_28
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