A new dynamic ensemble selection method for numeral recognition

/in , , /by
A new dynamic ensemble selection method for numeral recognition

Ko, Albert Hung Ren and Sabourin, Robert and De Britto, Alceu Souza

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2007

Abstract : An ensemble of classifiers (EoC) has been shown to be effective in improving classifier performance. To optimize EoC, the ensemble selection is one of the most imporatant issues. Dynamic scheme urges the use of different ensembles for different samples, but it has been shown that dynamic selection does not give better performance than static selection. We propose a dynamic selection scheme which explores the property of the oracle concept. The result suggests that the proposed scheme is apparently better than the selection based on popular majority voting error. © Springer-Verlag Berlin Heidelberg 2007.

You might also like
Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age
WEB image classification based on the fusion of image and text classifiers
DOPE: distributed optimization for pairwise energies
Privacy-cost management in smart meters with mutual information-based reinforcement learning
Heuristic search method to map computer tasks in parallel real-time electrical networks simulations
A survey of techniques for incremental learning of HMM parameters
Slow Wave Activity Optimization based on dominany peripheral nervous system oscillations
Unsupervised domain adaptation in the dissimilarity space for person re-identification