On the entropy of a hidden Markov process
Theoretical Computer Science
On the memory complexity of the forward-backward algorithm
Pattern Recognition Letters
Baum's forward-backward algorithm revisited
Pattern Recognition Letters
Pattern Recognition Letters
Dimensionality reduction using external context in pattern recognition problems with ordered labels
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 754.84 |
In many pattern-recognition problems there exist dependencies among the patterns to be recognized. In the past, these dependencies have not been introduced into the mathematical model when designing an optimal pattern-recognition system. In this paper the optimal decision rule is derived under the assumption of Markov dependence among the patterns to be recognized. Subsequently, this decision rule is applied to character-recognition problems. The main idea is to balance appropriately the information which is obtained from contextual considerations and the information from measurements on the character being recognized and thus arrive at a decision using both. Bayes' decision in Markov chains is presented and this mode of decision is adapted to character recognition. A look-ahead mode of decision is presented. The problem of estimation of transition probabilities is discussed. The experimental system is described and results of experiments on English legal text and names are presented.