On a Family of Decomposable Kernels on Sequences

Andrea Baisero, Florian T Pokorny, Carl Henrik Ek

Research output: Contribution to journalArticle (Academic Journal)

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In many applications data is naturally presented in terms of orderings of some basic elements or symbols. Reasoning about such data requires a notion of similarity capable of handling sequences of different lengths. In this paper we describe a family of Mercer kernel functions for such sequentially structured data. The family is characterized by a decomposable structure in terms of symbol-level and structure-level similarities, representing a specific combination of kernels which allows for efficient computation. We provide an experimental evaluation on sequential classification tasks comparing kernels from our family of kernels to a state of the art sequence kernel called the Global Alignment kernel which has been shown to outperform Dynamic Time Warping
Original languageEnglish
Number of pages12
Publication statusPublished - 26 Jan 2015


  • Kernel
  • Sequence


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