Perceptron Learning of Modified Quadratic Discriminant Function

Modified quadratic discriminant function (MQDF) is the state-of-the-art classifier in handwritten character recognition. Discriminative learning of MQDF can further improve its performance. Recent advances justify the efficacy of minimum classification error criteria in learning MQDF (MCE-MQDF). We provide an alternative choice to MCE-MQDF based on the Perceptron learning (PL-MQDF). For better generalization performance, we propose a new dynamic margin regularization. To relieve the heavy burden in training process, active set technique is employed, which can save most of the computation with negligible loss in accuracy. In experiments on handwritten digit datasets and a large-scale Chinese handwritten character database, the proposed PL-MQDF was demonstrated superior in both error reduction and training speedup.