Communications of the ACM
Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Feature analysis for symbol recognition by elastic matching
IBM Journal of Research and Development
Using new data to refine a Bayesian network
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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Our objective is to introduce Rissanen's Minimum Description Length (MDL) Principle as a useful tool for character recognition and present a first application. Using MDL principle, a learning system has been implemented which, after simple training, is able to online recognize th trainer's handwriting in English and Chinese (including several thousand characters) with high success rate. The experimental results conform with the theoretical predictions. We will also try to give an elegant explanation of Rissanen's minimum description length principle (MDLP).