Self-adaptive Topology Neural Network for Online Incremental Learning

Beatriz Pérez-Sánchez, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas

2014

Abstract

Many real problems in machine learning are of a dynamic nature. In those cases, the model used for the learning process should work in real time and have the ability to act and react by itself, adjusting its controlling parameters, even its structures, depending on the requirements of the process. In a previous work, the authors proposed an online learning method for two-layer feedforward neural networks that presents two main characteristics. Firstly, it is effective in dynamic environments as well as in stationary contexts. Secondly, it allows incorporating new hidden neurons during learning without losing the knowledge already acquired. In this paper, we extended this previous algorithm including a mechanism to automatically adapt the network topology in accordance with the needs of the learning process. This automatic estimation technique is based on the Vapnik-Chervonenkis dimension. The theoretical basis for the method is given and its performance is illustrated by means of its application to distint system identification problems. The results confirm that the proposed method is able to check whether new hidden units should be added depending on the requirements of the online learning process.

Paper Citation

in Harvard Style

Pérez-Sánchez B., Fontenla-Romero O. and Guijarro-Berdiñas B. (2014). Self-adaptive Topology Neural Network for Online Incremental Learning . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 94-101. DOI: 10.5220/0004811500940101

in Bibtex Style

@conference{icaart14,
author={Beatriz Pérez-Sánchez and Oscar Fontenla-Romero and Bertha Guijarro-Berdiñas},
title={Self-adaptive Topology Neural Network for Online Incremental Learning},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={94-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004811500940101},
isbn={978-989-758-015-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Self-adaptive Topology Neural Network for Online Incremental Learning
SN - 978-989-758-015-4
AU - Pérez-Sánchez B.
AU - Fontenla-Romero O.
AU - Guijarro-Berdiñas B.
PY - 2014
SP - 94
EP - 101
DO - 10.5220/0004811500940101