%0 Conference Paper %1 teredesai:2001:EuroGP %A Teredesai, Ankur %A Park, J. %A Govindaraju, Venugopal %B Genetic Programming, Proceedings of EuroGP'2001 %C Lake Como, Italy %D 2001 %E Miller, Julian F. %E Tomassini, Marco %E Lanzi, Pier Luca %E Ryan, Conor %E Tettamanzi, Andrea G. B. %E Langdon, William B. %I Springer-Verlag %K Active Character Digit Handwritten Pattern Recognition, algorithms, classification digit genetic programming, %P 371--379 %T Active Handwritten Character Recognition using Genetic Programming %U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=371 %V 2038 %X This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources used by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques. %@ 3-540-41899-7

@inproceedings{teredesai:2001:EuroGP, abstract = {This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources used by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Lake Como, Italy}, author = {Teredesai, Ankur and Park, J. and Govindaraju, Venugopal}, biburl = {https://www.bibsonomy.org/bibtex/2a7b6dca621ef5b82ab7a2e7ed524061a/brazovayeye}, booktitle = {Genetic Programming, Proceedings of EuroGP'2001}, editor = {Miller, Julian F. and Tomassini, Marco and Lanzi, Pier Luca and Ryan, Conor and Tettamanzi, Andrea G. B. and Langdon, William B.}, interhash = {85789aac9d0dca87ac9a9aa3aa1e5727}, intrahash = {a7b6dca621ef5b82ab7a2e7ed524061a}, isbn = {3-540-41899-7}, keywords = {Active Character Digit Handwritten Pattern Recognition, algorithms, classification digit genetic programming,}, month = {18-20 April}, notes = {EuroGP'2001, part of \cite{miller:2001:gp}}, organisation = {EvoNET}, pages = {371--379}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, size = {10 pages}, timestamp = {2008-06-19T17:53:04.000+0200}, title = {Active Handwritten Character Recognition using Genetic Programming}, url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=371}, volume = 2038, year = 2001 }