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Apprentissage incrémental en-ligne sur flux de données

Christophe Salperwyck 1, 2
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : Statistical learning provides numerous algorithms to build predictive models on past observations. These techniques proved their ability to deal with large scale realistic problems. However, new domains generate more and more data which are only visible once and need to be processed sequentially. These volatile data, known as data streams, come from telecommunication network management, social network, web mining. The challenge is to build new algorithms able to learn under these constraints. We proposed to build new summaries for supervised classi cation. Our summaries are based on two levels. The first level is an online incremental summary which uses low processing and address the precision/memory tradeo . The second level uses the first layer summary to build the fi nal summary with an e fficient offline method. Building these summaries is a pre-processing stage to develop new classifi ers for data streams. We propose new versions for the naive-Bayes and decision trees classi ers using our summaries. As data streams might contain concept drifts, we also propose a new technique to detect these drifts and up date classifi ers accordingly.
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Contributor : Philippe Preux <>
Submitted on : Wednesday, July 17, 2013 - 3:02:35 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:20 PM
Long-term archiving on: : Wednesday, April 5, 2017 - 1:21:16 PM


  • HAL Id : tel-00845655, version 1



Christophe Salperwyck. Apprentissage incrémental en-ligne sur flux de données. Apprentissage [cs.LG]. Université Charles de Gaulle - Lille III, 2012. Français. ⟨tel-00845655v1⟩



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