On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss

Denis Lecoeuche; Alex Aussem; Maxime Gasse

Journal articles

On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss

Denis Lecoeuche Alex Aussem ¹ Maxime Gasse ¹

1 DM2L - Data Mining and Machine Learning

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

Document type :

Journal articles

Domain :

Computer Science [cs] / Neural and Evolutionary Computing [cs.NE]

Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-02042711
Contributor : Alexandre Aussem <>
Submitted on : Wednesday, February 20, 2019 - 3:25:59 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:52 PM

On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss

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On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss

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