Kolcz, Aleksander and Allinson, Nigel
(1996)
*N-tuple regression network.*
Neural Networks, 9
(5).
pp. 855-869.
ISSN 08936080

Full content URL: http://www.sciencedirect.com/science/article/pii/0...

Full text not available from this repository.

Item Type: | Article |
---|---|

Item Status: | Live Archive |

## Abstract

N-tuple neural networks (NTNNs) have been successfully applied to both pattern recognition and function approximation tasks. Their main advantages include a single layer structure, capability ofrealizing highly non-linear mappings and simplicity of operation. In this work a modification of the basic network architecture is presented, which allows it to operate as a non-parametric kernel regression estimator. This type of network is inherently capable of approximating complex probability density functions (pdfs) and, in the limiting sense, deterministic arbitrary function mappings. At the same time, the regression network features a powerful one-pass training procedure and its learning is statistically consistent. The major advantage of utilizing the N-tuple architecture are a regression estimator is the fact that in this realization the training set points are stored by the network implicitly, rather that explicitly, and thus the operation speed remains constant and independent of the training set size. Therefore, the network performance can be guaranteed in practical implementations. Copyright © 1996 Elsevier Science Ltd

Keywords: | Conformal mapping, Learning systems, Parameter estimation, Probability density function, Random access storage, Sampling, Basis functions, Local receptive fields, N tuple regression network, N tuple sampling, Non parametric estimation, Random access storage based neural networks, Regression equation, Neural networks, article, artificial neural network, priority journal, receptive field |
---|---|

Subjects: | G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G730 Neural Computing |

Divisions: | College of Science > School of Computer Science |

ID Code: | 8598 |

Deposited On: | 26 Apr 2013 11:09 |

Repository Staff Only: item control page