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Author: David Edelman

Affiliation: University College Dublin and Ireland

Keyword(s): Machine Learning, Data Compression, Information Theory, Unsupervised Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: The latent binary variable training problem used in the pre-training process for Deep Neural Networks is approached using the Principle (and related Criterion) of Maximum Mutual Information (MMI). This is presented as an alternative to the most widely-accepted ’Restricted Boltzmann Machine’ (RBM) approach of Hinton. The primary contribution of the present article is to present the MMI approach as the arguably more logically ’natural’ and logically simple means to the same ends. Additionally, the relative ease and effectiveness of the approach for application will be demonstrated for an example case.

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Paper citation in several formats:
Edelman, D. (2019). An Alternative to Restricted-Boltzmann Learning for Binary Latent Variables based on the Criterion of Maximal Mutual Information. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 865-868. DOI: 10.5220/0007618608650868

@conference{icaart19,
author={David Edelman.},
title={An Alternative to Restricted-Boltzmann Learning for Binary Latent Variables based on the Criterion of Maximal Mutual Information},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={865-868},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007618608650868},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - An Alternative to Restricted-Boltzmann Learning for Binary Latent Variables based on the Criterion of Maximal Mutual Information
SN - 978-989-758-350-6
IS - 2184-433X
AU - Edelman, D.
PY - 2019
SP - 865
EP - 868
DO - 10.5220/0007618608650868
PB - SciTePress

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