A supervised strategy for deep kernel machine
Florian Yger, Maxime Berar, Gilles Gasso and Alain Rakotomamonjy
In: ESANN 2011, 27 - 29 April 2011, Bruges, Belgium.

Abstract

This paper presents an alternative to the supervised KPCA based approach for learning a Multilayer Kernel Machine (MKM) . In our proposed procedure, the hidden layers are learnt in a supervised fash- ion based on kernel partial least squares regression. The main interest resides in a simplified learning scheme as the obtained hidden features are automatically ranked according to their correlation with the target out- puts. The approach is illustrated on small scale real world applications and shows compelling evidences.

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