Improved Classification Based on Deep Belief Networks

Jaehoon Koo, Diego Klabjan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


For better classification, generative models are used to initialize the model and extract features before training a classifier. Typically, separate unsupervised and supervised learning problems are solved. Generative restricted Boltzmann machines and deep belief networks are widely used for unsupervised learning. We developed several supervised models based on deep belief networks in order to improve this two-phase strategy. Modifying the loss function to account for expectation with respect to the underlying generative model, introducing weight bounds, and multi-level programming are all applied in model development. The proposed models capture both unsupervised and supervised objectives effectively. The computational study verifies that our models perform better than the two-phase training approach. In addition, we conduct an ablation study to examine how a different part of our model and a different mix of training samples affect the performance of our models.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030616083
StatePublished - 2020
Event29th International Conference on Artificial Neural Networks, ICANN 2020 - Bratislava, Slovakia
Duration: Sep 15 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12396 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Artificial Neural Networks, ICANN 2020


  • Classification
  • Deep belief networks
  • Deep learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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