A general regression technique for learning transductions

Corinna Cortes, Mehryar Mohri, Jason Weston

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

Abstract

The problem of learning a transduction, that is a string-to-string mapping, is a common problem arising in natural language processing and computational biology. Previous methods proposed for learning such mappings are based on classification techniques. This paper presents a new and general regression technique for learning transductions and reports the results of experiments showing its effectiveness. Our transduction learning consists of two phases: the estimation of a set of regression coefficients and the computation of the pre-image corresponding to this set of coefficients. A novel and conceptually cleaner formulation of kernel dependency estimation provides a simple framework for estimating the regression coefficients, and an efficient algorithm for computing the pre-image from the regression coefficients extends the applicability of kernel dependency estimation to output sequences. We report the results of a series of experiments illustrating the application of our regression technique for learning transductions.

Original languageEnglish (US)
Title of host publicationICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
EditorsL. Raedt, S. Wrobel
Pages153-160
Number of pages8
DOIs
StatePublished - Dec 1 2005
EventICML 2005: 22nd International Conference on Machine Learning - Bonn, Germany
Duration: Aug 7 2005Aug 11 2005

Publication series

NameICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Other

OtherICML 2005: 22nd International Conference on Machine Learning
CountryGermany
CityBonn
Period8/7/058/11/05

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Cortes, C., Mohri, M., & Weston, J. (2005). A general regression technique for learning transductions. In L. Raedt, & S. Wrobel (Eds.), ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning (pp. 153-160). (ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning). https://doi.org/10.1145/1102351.1102371