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2017 | OriginalPaper | Buchkapitel

PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach

verfasst von : Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models

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The fusion of descriptions, resp. of models, is sometimes called Early Fusion, resp. Late Fusion.

Our notion of hyper-prior and hyper-posterior distributions is different than the one proposed for lifelong learning [25], where they basically consider hyper-prior and hyper-posterior over the set of possible priors: The prior distribution P over the voters’ set is viewed as a random variable.

https://archive.ics.uci.edu/ml/datasets/Reuters+RCV1+RCV2+Multilingual,+Multiview+Text+Categorization+Test+collection.

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Titel: PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach
verfasst von: Anil Goyal
Emilie Morvant
Pascal Germain
Massih-Reza Amini
Verlag: Springer International Publishing
Buch: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-319-71245-1

Electronic ISBN: 978-3-319-71246-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71246-8_13

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