Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Feature Selection for Ensembles: A Hierarchical Multi-Objective Genetic Algorithm Approach
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A new HMM-based ensemble generation method for numeral recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Classification ensemble by genetic algorithms
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Classifier selection by clustering
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
A new adaptive framework for classifier ensemble in multiclass large data
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
A heuristic classifier ensemble for huge datasets
AMT'11 Proceedings of the 7th international conference on Active media technology
A new classifier ensembles framework
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
A scalable heuristic classifier for huge datasets: a theoretical approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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The study of multiple classifier systems has become an area of intensive research in pattern recognition recently. Also in handwriting recognition, systems combining several classifiers have been investigated. In this paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. Those new methods are evaluated and compared to existing approaches in the context of handwritten word recognition, using a hidden Markov model recognizer as basic classifier.