Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A Methodology for Mapping Scores to Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Adaptation in Recognition of Handwritten Alphanumeric Characters
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Multiple Recognizers System Using Two-Stage Combination
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Ensemble classification by critic-driven combining
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Neural and statistical classifiers-taxonomy and two case studies
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
There are two main approaches to classifier adaptation. A single adaptive classfier 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 committeees can improve on the recognition results, both in comparison to the individual member classifiers and the non-adaptive reference committee.