Similarity-driven sequence classification based on support vector machines
Haoyu Lei, Venu Govindaraju · 2005
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 ER/sup 2/, 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 ER/sup 2/ is above 90%), it is safe to assign X to the same class as Y. ER/sup 2/ 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 online handwriting recognition, where handwritten characters are represented by 2D sequences of X-, Y-coordinates. Experiments on the benchmark database UNIPEN show that the classification driven by ER/sup 2/ can be about three times faster than standard SVM while the classification accuracy is enhanced or comparable.