Abstract
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.
Original language | English |
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Pages (from-to) | 50-65 |
Journal | Theoretical Computer Science |
Volume | 495 |
DOIs | |
Publication status | Published (in print/issue) - 15 Jul 2013 |