Sparsification of Linear Models for Large-Scale Text Classification

Abstract : In this paper we propose a simple yet effective method for sparsifying a posteriori linear models for large-scale text classification. The objective is to maintain high performance while reducing the prediction time by producing very sparse models. This is especially important in real-case scenarios where one deploys predictive models in several machines across the network and constraints apply on the prediction time. We empirically evaluate the proposed approach in a large collection of documents from the Large-Scale Hierarchical Text Classification Challenge. The comparison with a feature selection method and LASSO regularization shows that we achieve to obtain a sparse representation improving in the same time the classification performance.
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Communication dans un congrès
Conférence sur l'APprentissage automatique (CAp 2015), Jul 2015, Lille, France. 〈http://cap2015.sciencesconf.org/〉
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https://hal.archives-ouvertes.fr/hal-01236591
Contributeur : Massih-Reza Amini <>
Soumis le : mardi 1 décembre 2015 - 22:24:25
Dernière modification le : mercredi 2 décembre 2015 - 01:06:49

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  • HAL Id : hal-01236591, version 1

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Simon Moura, Ioannis Partalas, Massih-Reza Amini. Sparsification of Linear Models for Large-Scale Text Classification. Conférence sur l'APprentissage automatique (CAp 2015), Jul 2015, Lille, France. 〈http://cap2015.sciencesconf.org/〉. 〈hal-01236591〉

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