1 Hybrid DNA Sequence Similarity Scheme for Training Support Vector Machines *

Similarity between two DNA sequences is based on alignment. There are different approaches of alignments; each has its own specialty of bearing different information on DNA sequence. This paper presents a study on similarity kernels based on different similarity schemes and proposes a hybrid one. Similarity Kernel is required in order to represent the distance or similarity between two DNA sequences. The different schemes of alignments and the cost of computing them, make it further more difficult to decide what scheme to use. In this study we combine different similarity schemes; each scheme is deduced based on alignment. We demonstrate that combining different similarity scheme does in fact generalize well in machine learning. The scoring scheme also turned to have impact on generalization. 1