Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification

Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences. A commonly used notion of similarity is the number of matches between k-mers (k-length subsequences) in the two sequences. Extending this definition, by considering two k-mers to match if their distance is at most m, yields better classification performance. This, however, makes the problem computationally much more complex. Known algorithms to compute this similarity have computational complexity that render them applicable only for small values of k and m.