A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.

We report on progress in handwriting recognition and signature verification. Our system, which uses pen-trajectory information, is suitable for use in pen-based computers. It has a multi-modular architecture whose central trainable module is a Time Delay Neural Network. Results comparing our system and a commercial recognizer are presented. Our best recognizer make three times less errors on hand-printed word recognition than the commercial one.