Radial basis function approximations to polynomials
Numerical analysis 1987
Computer
The nature of statistical learning theory
The nature of statistical learning theory
OCR in a Hierarchical Feature Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Genetic Algorithm for Feature Selection in a Neuro-Fuzzy OCR System
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A smart hill-climbing algorithm for application server configuration
Proceedings of the 13th international conference on World Wide Web
An improved handwritten Chinese character recognition system using support vector machine
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
A hierarchical approach to recognition of handwritten Bangla characters
Pattern Recognition
Editorial: New Frontiers in Handwriting Recognition
Pattern Recognition
Recognition of Numeric Postal Codes from Multi-script Postal Address Blocks
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Feature selection using genetic algorithm and cluster validation
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Handwritten bangla digit recognition using classifier combination through DS technique
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
Handwritten character recognition system using a simple feature
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
A hybrid OC-GA approach for fast and global truss optimization with frequency constraints
Applied Soft Computing
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Identification of local regions from where optimal discriminating features can be extracted is one of the major tasks in the area of pattern recognition. To locate such regions different kind of region sampling techniques are used in the literature. There is no standard methodology to identify exactly such regions. Here we have proposed a methodology where local regions of varying heights and widths are created dynamically. Genetic algorithm (GA) is then applied on these local regions to sample the optimal set of local regions from where an optimal feature set can be extracted that has the best discriminating features. We have evaluated the proposed methodology on a data set of handwritten Bangla digits. In the present work, we have randomly generated seven sets of local regions and from every set, GA selects an optimal group of local regions which produces best recognition performance with a support vector machine (SVM) based classifier. Other popular optimization techniques like simulated annealing (SA) and hill climbing (HC) have also been evaluated with the same data set and maximum recognition accuracies were found to be 97%, 96.7% and 96.7% for GA, SA and HC, respectively. We have also compared the performance of the present technique with those of other zone based techniques on the same database.