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Fast CNN Pruning via Redundancy-Aware Training Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests

Authors : Maxime Sazadaly, Pierre Pinchon, Arthur Fagot, Lionel Prevost, Myriam Maumy Bertrand

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

Hierarchical systems are powerful tools to deal with non-linear data with a high variability. We show in this paper that regressing a bounded variable on such data is a challenging task. As an alternate, we propose here a two-step process. First, an ensemble of ordinal classifiers affect the observation to a given range of the variable to predict and a discrete estimate of the variable. Then, a regressor is trained locally on this range and its neighbors and provides a finer continuous estimate. Experiments on affect audio data from the AVEC’2014 and AV+EC’2015 challenges show that this cascading process can be compared favorably to the state of the art and challengers results.

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Metadata
Title
Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests
Authors
Maxime Sazadaly
Pierre Pinchon
Arthur Fagot
Lionel Prevost
Myriam Maumy Bertrand
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_75

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