PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

SEDiL: Software for Edit Distance Learning
Laurent Boyer, Yann Esposito, Amaury Habrard, Jose Oncina and Marc Sebban
In: ECML PKDD 2008, 15 - 19 September 2008, Antwerp, Belgium.


In this paper, we present SEDiL, a Software for Edit Distance Learning. SEDiL is an innovative prototype implementation grouping together most of the state of the art methods that aim to automatically learn the parameters of string and tree edit distances.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4157
Deposited By:Laurent Boyer
Deposited On:24 October 2008