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ICML
2005
IEEE

A general regression technique for learning transductions

13 years 22 days ago
A general regression technique for learning transductions
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...
Corinna Cortes, Mehryar Mohri, Jason Weston
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Corinna Cortes, Mehryar Mohri, Jason Weston
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