From Cutting Planes Algorithms to Compression Schemes and Active Learning

Ugo Louche 1 Liva Ralaivola 1
1 QARMA - éQuipe AppRentissage et MultimediA [Marseille]
LIF - Laboratoire d'informatique Fondamentale de Marseille
Abstract : Cutting-plane methods are well-studied localization (and optimization) algorithms. We show that they provide a natural framework to perform machine learning ---and not just to solve optimization problems posed by machine learning--- in addition to their intended optimization use. In particular, they allow one to learn sparse classifiers and provide good compression schemes. Moreover, we show that very little effort is required to turn them into effective active learning methods. This last property provides a generic way to design a whole family of active learning algorithms from existing passive methods. We present numerical simulations testifying of the relevance of cutting-plane methods for passive and active learning tasks.
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Contributor : Ugo Louche <>
Submitted on : Monday, August 10, 2015 - 10:18:00 AM
Last modification on : Monday, March 4, 2019 - 2:04:23 PM
Long-term archiving on : Wednesday, November 11, 2015 - 10:12:46 AM


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  • HAL Id : hal-01182767, version 1
  • ARXIV : 1508.02986



Ugo Louche, Liva Ralaivola. From Cutting Planes Algorithms to Compression Schemes and Active Learning. IJCNN 2015, Jul 2015, Killarney, Ireland. ⟨hal-01182767⟩



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