Versatile string kernels

Cees Elzinga, Hui Wang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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 languageEnglish
Pages (from-to)50-65
JournalTheoretical Computer Science
Publication statusPublished (in print/issue) - 15 Jul 2013


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