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PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach

Anil Goyal 1, 2 Emilie Morvant 1 Pascal Germain 3 Massih-Reza Amini 2
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the second level. Our main theoretical result is a generalization bound on the risk of the majority vote which exhibits a term of diversity in the predictions of the view-specific classifiers. From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. Finally, we experiment our principle on multiview datasets extracted from the Reuters RCV1/RCV2 collection.
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https://hal.archives-ouvertes.fr/hal-01546109
Contributor : Emilie Morvant <>
Submitted on : Monday, July 24, 2017 - 5:57:30 PM
Last modification on : Monday, April 20, 2020 - 11:24:02 AM
Document(s) archivé(s) le : Saturday, January 27, 2018 - 3:05:49 AM

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  • HAL Id : hal-01546109, version 1
  • ARXIV : 1606.07240

Citation

Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini. PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach. European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Sep 2017, Skopje, Macedonia. ⟨hal-01546109⟩

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