Discriminative Training of the Scanning N-Tuple Classifier

The Scanning N-Tuple classifier (SNT) was introduced by Lucas and Amiri [1, 2] as an efficient and accurate classifier for chaincoded hand-written digits. The SNT operates as speeds of tens of thousands of sequences per second, during both the training and the recognition phases. The main contribution of this paper is to present a new discriminative training rule for the SNT. Two versions of the rule are provided, based on minimizing the mean-squared error and the cross-entropy, respectively. The discriminative training rule offers improved accuracy at the cost of slower training time, since the training is now iterative instead of single pass. The cross-entropy trained SNT offers the best results, with an error rate of 2.5% on sequences derived from the MNIST test set.