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• W2894526900 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." @default.
• W2894526900 created "2018-10-05" @default.
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• W2894526900 date "2018-01-01" @default.
• W2894526900 modified "2023-09-28" @default.
• W2894526900 title "Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests" @default.
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• W2894526900 doi "https://doi.org/10.1007/978-3-030-01418-6_75" @default.
• W2894526900 hasPublicationYear "2018" @default.
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