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Authors: Beatriz Pérez-Sánchez ; Oscar Fontenla-Romero and Bertha Guijarro-Berdiñas

Affiliation: University of A Coruña, Spain

ISBN: 978-989-758-015-4

Keyword(s): Incremental Learning, Sequential Learning, Forgetting Ability, Adaptive Topology, Vapnik-Chervonenkis Dimension.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; 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: 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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