loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Athanasios Tsakonas 1 and Georgios Dounias 2

Affiliations: 1 Demokritus University of Thrace, Greece ; 2 University of the Aegean, Greece

Abstract: Artificial neural networks have been universally acknowledged for their ability on constructing forecasting and classifying systems. Among their desirable features, it has always been the interpretation of their structure, aiming to provide further knowledge for the domain experts. A number of methodologies have been developed for this reason. One such paradigm is the neural logic networks concept. Neural logic networks have been especially designed in order to enable the interpretation of their structure into a number of simple logical rules and they can be seen as a network representation of a logical rule base. Although powerful by their definition in this context, neural logic networks have performed poorly when used in approaches that required training from data. Standard training methods, such as the back-propagation, require the network’s synapse weight altering, which destroys the network’s interpretability. The methodology in this paper overcomes these problems and proposes an architecture-altering technique, which enables the production of highly antagonistic solutions while preserving any weight-related information. The implementation involves genetic programming using a grammar-guided training approach, in order to provide arbitrarily large and connected neural logic networks. The methodology is tested in a problem from the banking sector with encouraging results. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.220.97.161

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Tsakonas, A. and Dounias, G. (2005). An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the banking sector. In Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2005) - ANNIIP; ISBN 972-8865-36-8, SciTePress, pages 82-93. DOI: 10.5220/0001192100820093

@conference{anniip05,
author={Athanasios Tsakonas. and Georgios Dounias.},
title={An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the banking sector},
booktitle={Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2005) - ANNIIP},
year={2005},
pages={82-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001192100820093},
isbn={972-8865-36-8},
}

TY - CONF

JO - Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2005) - ANNIIP
TI - An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the banking sector
SN - 972-8865-36-8
AU - Tsakonas, A.
AU - Dounias, G.
PY - 2005
SP - 82
EP - 93
DO - 10.5220/0001192100820093
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic