Similarity-driven sequence classification based on support vector machines

@article{Lei2005SimilaritydrivenSC,
  title={Similarity-driven sequence classification based on support vector machines},
  author={Hansheng Lei and Venu Govindaraju},
  journal={Eighth International Conference on Document Analysis and Recognition (ICDAR'05)},
  year={2005},
  pages={252-256 Vol. 1}
}
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… CONTINUE READING

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