Versatile string kernels

C.H. Elzinga, X. Wang

Research output: Contribution to JournalArticleAcademicpeer-review


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. © 2013 Elsevier B.V.
Original languageEnglish
Pages (from-to)50-65
Number of pages15
JournalTheoretical Computer Science
Early online date16 Jun 2013
Publication statusPublished - 2013


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