The String-to-String Correction Problem
Journal of the ACM (JACM)
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Automatic Construction of 2D Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Median strings for k-nearest neighbour classification
Pattern Recognition Letters
Comparison of AESA and LAESA search algorithms using string and tree-edit-distances
Pattern Recognition Letters
A Learning Model for Multiple-Prototype Classification of Strings
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A stochastic approach to wilson's editing algorithm
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
An improved fast edit approach for two-string approximated mean computation applied to OCR
Pattern Recognition Letters
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This paper presents a new fast algorithm to compute an approximation to the median between two strings of characters representing a 2D shape and its application to a new classification scheme to decrease its error rate. The median string results from the application of certain edit operations from the minimum cost edit sequence to one of the original strings. The new dataset editing scheme relaxes the criterion to delete instances proposed by the Wilson Editing Procedure. In practice, not all instances misclassified by its near neighbors are pruned. Instead, an artificial instance is added to the dataset expecting to successfully classify the instance on the future. The new artificial instance is the median from the misclassified sample and its same-class nearest neighbor. The experiments over two widely used datasets of handwritten characters show this preprocessing scheme can reduce the classification error in about 78% of trials.