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2017

Deep Incremental Boosting

Mosca, Alan, Magoulas, George D

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This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation.

We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member.
We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep Incremental Boosting brings to traditional Ensemble methods in Deep Learning.

Deep Incremental Boosting · Around

Built on

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R. E. Schapire · 1990
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Experiments with a new boosting algorithm
R. E. Schapire and Y Freund · 1996
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An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
Thomas G Dietterich · 2000
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
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