Similarity-driven sequence classification based on support vector machines

A novel sequence classification method is proposed in the context of Support Vector Machines (SVM). This method is driven by an intuitive similarity measure, namely ER², which directly tells the similarity of two sequences (1- or multi-dimensional). If sequence X is very similar to Y (for instance, the similarity by ER² is above 90%), it is safe to assign X to the same class as Y. ER² is plugged into standard SVM to speed up the decision-making of multi-class classification. The immediate application of the method is in the adaptive on-line handwriting recognition, where handwritten characters are represented by 2D sequences of X-,Y-coordinates. Experiments on the benchmark database UN/PEN show that the classification driven by ER² can be about three times faster than standard SVM while the classification accuracy is enhanced or comparable.