Rational kernels

Corinna Cortes, Patrick Haffner, Mehryar Mohri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We introduce a general family of kernels based on weighted transducers or rational relations, rational kernels, that can be used for analysis of variable-length sequences or more generally weighted automata, in applications such as computational biology or speech recognition. We show that rational kernels can be computed efficiently using a general algorithm of composition of weighted transducers and a general single-source shortest-distance algorithm. We also describe several general families of positive definite symmetric rational kernels. These general kernels can be combined with Support Vector Machines to form efficient and powerful techniques for spoken-dialog classification: highly complex kernels become easy to design and implement and lead to substantial improvements in the classification accuracy. We also show that the string kernels considered in applications to computational biology are all specific instances of rational kernels.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
StatePublished - 2003
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: Dec 9 2002Dec 14 2002

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other16th Annual Neural Information Processing Systems Conference, NIPS 2002
CityVancouver, BC

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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