Rational Kernels

Corinna Cortes, Patrick Haffner, Mehryar Mohri

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

Abstract

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 publicationNIPS 2002
Subtitle of host publicationProceedings of the 15th International Conference on Neural Information Processing Systems
EditorsSuzanna Becker, Sebastian Thrun, Klaus Obermayer
PublisherMIT Press Journals
Pages601-608
Number of pages8
ISBN (Electronic)0262025507, 9780262025508
StatePublished - 2002
Event15th International Conference on Neural Information Processing Systems, NIPS 2002 - Vancouver, Canada
Duration: Dec 9 2002Dec 14 2002

Publication series

NameNIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Conference

Conference15th International Conference on Neural Information Processing Systems, NIPS 2002
Country/TerritoryCanada
CityVancouver
Period12/9/0212/14/02

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications
  • Information Systems

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