PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sequence Labelling SVMs Trained in One Pass
Antoine Bordes, Nicolas Usunier and Léon Bottou
In: ECML PKDD 2008, 15-19 Sept 2008, Antwerp, Belgium.

Abstract

This paper proposes an online solver of the dual formulation of support vector machines for structured output spaces. We apply it to sequence labelling using the exact and greedy inference schemes. In both cases, the per-sequence training time is the same as a perceptron based on the same inference procedure, up to a small multiplicative constant. Comparing the two inference schemes, the greedy version is much faster. It is also amenable to higher order Markov assumptions and performs similarly on test. In comparison to existing algorithms, both versions match the accuracies of batch solvers that use exact inference after a single pass over the training examples.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5324
Deposited By:Nicolas Usunier
Deposited On:24 March 2009