Hybrid Learning of Regularization Neural Networks

Authors: Petra Vidnerová, Roman Neruda
Publication year: 2010
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Artificial Intelligence and Soft Computing
Projects: Neural Networks Learning Algorithms Based on Regularization Theory
Bibliographic citation: VIDNEROVÁ, Petra, NERUDA, Roman. Hybrid Learning of Regularization Neural Networks. In: RUTKOWSKI, L., SCHERER, R., TADEUSIEWICZ, R., ZADEH, L.A., ZURADA, J.M., eds. Artificial Intelligence and Soft Computing. Vol. 2. Berlin: Springer, 2010, S. 124-131. Lecture Notes in Artificial Intelligence, 6114. ISBN 978-3-642-13231-5. ISSN 0302-9743
Dostupné z: doi:10.1007/978-3-642-13232-2_15
DOI: 10.1007/978-3-642-13232-2_15
ID: 345012

Abstract

Regularization theory presents a sound framework to solving supervised learning problems. However, the regularization networks have a large size corresponding to the size of training data. In this work we study a relationship between network complexity, i.e. number of hidden units, and approximation and generalization ability. We propose an incremental hybrid learning algorithm that produces smaller networks with performance similar to original regularization networks.