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Abstract

We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the {0,1}-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This paper shows that this function can be extended to the Binomial and {−1,+1}-Bernoulli hidden units.

Item Type:	Conference or Workshop Item (Paper)
Authors/Creators:	Cherla, S and Tran, SN and d'Avila Garcez, A and Weyde, T
Keywords:	discriminative learning, hidden layer activation function, restricted Boltzmann machine
Journal or Publication Title:	Proceedings of the 26th International Conference on Artificial Neural Networks: Artificial Neural Networks and Machine Learning, Part II
Publisher:	Springer
DOI / ID Number:	10.1007/978-3-319-68612-7_13
Copyright Information:	Copyright 2017 Springer
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