Pairwise classification and support vector machines
Advances in kernel methods
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
ER2: An Intuitive Similarity Measure for On-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Parametric kernels for sequence data analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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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 UNIPEN show that the classification driven by ER2 can be about three times faster than standard SVM while the classification accuracy is enhanced or comparable.