A novel prototype generation technique for handwriting digit recognition

The aim of this paper is to introduce a novel prototype generation technique for handwriting digit recognition. Prototype generation is approached as a two-stage process. The first stage uses an Adaptive Resonance Theory 1 (ART1) based algorithm to select an effective initial solution, while the second one executes a fine tuning designed to generate the best prototypes. To this end, the second stage deals with an optimization problem, in which the objective function to be minimized is the cost function associated to the classification. A naive evolution strategy is used to generate the prototype set able to reduce classification time, without greatly affecting the accuracy. Moreover, as the ART1 based algorithm has incremental learning capability, the first stage is also useful for selecting the prototype set according to variations in handwriting style. The classification task is performed by the k-nearest neighbor classifier. Experimental tests on the MNIST dataset demonstrated that our technique represents a good trade-off among accuracy, classification speed and robustness to handwriting style changes

Learning Iterative Strategies in Multi-Expert Systems using SVMs for Digit Recognition

This paper presents three different learning iterative strategies, in a multi-expert system. In first strategy entire new dataset is used. In second strategy each single classifier selects new samples starting from those on which it performs a misclassification. Finally, the collective behavior of classifiers is studied to select the most profitable samples for knowledge base updating. The experimental results provide a comparison of three approaches under different operating conditions and feedback process. A classifier SVM and four different combination techniques were used by considering the CEDAR (handwritten digit) database. It is shown how results depend by the iterations on the feedback process, as well as by the specific combination decision schema and by data distribution