Class-confidence critic combining

This paper discusses a combination of two techniques for improving the recognition accuracy of on-line handwritten character recognition: committee classification and adaptation to the user. A novel adaptive committee structure, namely the class-confidence critic combination (CCCC) scheme, is presented and evaluated. It is shown to be able to improve significantly on its member classifiers. Also the effect of having either more or less diverse sets of member classifiers is considered.

[1]  David J. Miller,et al.  Critic-driven ensemble classification , 1999, IEEE Trans. Signal Process..

[2]  Harris Drucker,et al.  Boosting Performance in Neural Networks , 1993, Int. J. Pattern Recognit. Artif. Intell..

[3]  Chunheng Wang,et al.  Adaptive combination of classifiers and its application to handwritten Chinese character recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[5]  Ching Y. Suen,et al.  A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Erkki Oja,et al.  Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters , 2001, International Journal on Document Analysis and Recognition.