A Fast Learning Strategy Using Pattern Selection for Feedforward Neural Networks

Szilárd Vajda; Yves Rangoni; Hubert Cecotti; Abdel Belaïd

A Fast Learning Strategy Using Pattern Selection for Feedforward Neural Networks

Szilárd Vajda ¹ Yves Rangoni ¹ Hubert Cecotti ¹ Abdel Belaïd ¹

1 READ - READ

LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications

Abstract : Intelligent pattern selection is an active learning strategy where the classifiers select during training the most informative patterns. This paper investigates such a strategy where the informativeness of a pattern is measured as the approximation error produced by the classifier. The algorithm builds the training corpus starting from a small randomly chosen initial dataset and new patterns are added to the learning corpus based on their error sensitivity. The training dataset expansion is based on the selection of the most erroneous patterns. Our experimental results on MNIST 1 separated digit dataset show that only 3.26%of training data are sufficient for training purpose without decreasing the performance (98.36%) of the resulting neural classifier.

Keywords : Pattern selection incremental learning handwritten digit recognition neural networks support vector machines

Document type :

Conference papers

Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition 2006 - IWFHR'10, Oct 2006, La Baule, France. Suvisoft, pp.6, 2006

Domain :

Computer Science [cs] / Computer Vision and Pattern Recognition [cs.CV]

Computer Science [cs] / Document and Text Processing

https://hal.inria.fr/inria-00104833
Contributor : Anne Jaigu <>
Submitted on : Monday, October 9, 2006 - 2:59:40 PM
Last modification on : Thursday, May 19, 2016 - 1:05:43 AM
Document(s) archivé(s) le : Tuesday, April 6, 2010 - 7:02:39 PM

A Fast Learning Strategy Using Pattern Selection for Feedforward Neural Networks

File

Identifiers

Collections

Citation

Export

Share

Metrics

122

70

Contact