PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Boosting Grammatical Inference with Confidence Oracles
Janodet, J-C Janodet, Nock, R Nock, Marc Sebban and Henri-Maxime Suchier
In: International Conference on Machine Learning, 4-8 july 2004, Banff, Alberta, Canada.


In this paper we focus on the adaptation of boosting to grammatical inference. We aim at improving the performances of state merging algorithms in the presence of noisy data by using, in the update rule, additional information provided by an oracle. This strategy requires the construction of a new weighting scheme that takes into account the confidence in the labels of the examples. We prove that our new framework preserves the theoretical properties of boosting. Using the state merging algorithm rpni*, we describe an experimental study on various datasets, showing a dramatic improvement of performances.

Postscript - Requires a viewer, such as GhostView
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:92
Deposited By:Henri-Maxime Suchier
Deposited On:18 May 2004