A Comparison of Learning Rules for Mixed Order Hyper Networks

Kevin Swingler

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Paper

A Comparison of Learning Rules for Mixed Order Hyper Networks

Topics: Learning Paradigms and Algorithms; Neural Based Data Mining and Complex Information Processing; Supervised and Unsupervised Learning

In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: ECTA, 17-27, 2015 , Lisbon, Portugal

Author: Kevin Swingler

Affiliation: University of Stirling, United Kingdom

Keyword(s): High Order Networks, Learning Rules.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: A mixed order hyper network (MOHN) is a neural network in which weights can connect any number of neurons, rather than the usual two. MOHNs can be used as content addressable memories with higher capacity than standard Hopfield networks. They can also be used for regression, clustering, classification, and as fitness models for use in heuristic optimisation. This paper presents a set of methods for estimating the values of the weights in a MOHN from training data. The different methods are compared to each other and to a standard MLP trained by back propagation and found to be faster to train than the MLP and more reliable as the error function does not contain local minima.

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Paper citation in several formats:

Swingler, K. (2015). A Comparison of Learning Rules for Mixed Order Hyper Networks. In Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA; ISBN 978-989-758-157-1, SciTePress, pages 17-27. DOI: 10.5220/0005588000170027

@conference{ncta15,
author={Kevin Swingler.},
title={A Comparison of Learning Rules for Mixed Order Hyper Networks},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA},
year={2015},
pages={17-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005588000170027},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA
TI - A Comparison of Learning Rules for Mixed Order Hyper Networks
SN - 978-989-758-157-1
AU - Swingler, K.
PY - 2015
SP - 17
EP - 27
DO - 10.5220/0005588000170027
PB - SciTePress