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Sequence Labelling SVMs Trained in One Pass

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.
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Contributor : Antoine Bordes <>
Submitted on : Thursday, November 15, 2012 - 3:17:23 PM
Last modification on : Friday, January 8, 2021 - 5:34:11 PM

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Antoine Bordes, Nicolas Usunier, Léon Bottou. Sequence Labelling SVMs Trained in One Pass. ECML PKDD 2008 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2008, Anvers, Belgium. pp.146-161, ⟨10.1007/978-3-540-87479-9_28⟩. ⟨hal-00752369⟩



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