This paper proposes a class of string kernels that can handle a variety of subsequence-based features. Slight adaptations of the basic algorithm allow for weighing subsequence lengths, restricting or soft-penalizing gap-size, character-weighing and soft-matching of characters. An easy extension of the kernels allows for comparing run-length encoded strings with a time-complexity that is independent of the length of the original strings. Such kernels have applications in image processing, computational biology, in demography and in comparing partial rankings.
|Number of pages||16|
|Journal||Theoretical Computer Science|
|Early online date||14 Jun 2013|
|Publication status||Published - 15 Jul 2013|