Exploring Pattern Selection Strategies for Fast Neural Network Training

Nowadays, the usage of neural network strategies in pattern recognition is a widely considered solution. In this paper we propose three different strategies to select more efficiently the patterns for a fast learning in such a neural framework by reducing the number of available training patterns. All the strategies rely on the idea of dealing just with samples close to the decision boundaries of the classifiers. The effectiveness (accuracy, speed) of these methods is confirmed through different experiments on the MNIST handwritten digit data [1], Bangla handwritten numerals [2] and the Shuttle data from the UCI machine learning repository [3].