Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks

Alan Thomas; Simon Walters; Miltiadis Petridis; Mohammad Malekshahi Gheytassi; Robert Morgan

doi:10.1007/978-3-319-44188-7_19

Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks

Alan Thomas, Simon Walters, Miltiadis Petridis, Mohammad Malekshahi Gheytassi, Robert Morgan

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Two-hidden-layer feedforward neural networks are investigated for the existence of an optimal hidden node ratio. In the experiments, the heuristic n1 =int (0.5nh + 1), where n1 is the number of nodes in the first hidden layer and nh is the total number of hidden nodes, found networks with generalisation errors, on average, just 0.023%-0.056% greater than those found by exhaustive search. This reduced the complexity of an exhaustive search from quadratic, to linear in , with very little penalty. Further reductions in search complexity to logarithmic could be possible using existing methods developed by the Authors

Original language	English
Title of host publication	Engineering Applications of Neural Networks. EANN 2016
Place of Publication	Switzerland
Publisher	Springer International Publishing
Pages	253-266
Number of pages	14
ISBN (Electronic)	9783319441887
ISBN (Print)	9783319441870
DOIs	https://doi.org/10.1007/978-3-319-44188-7_19
Publication status	Published - 19 Aug 2016
Event	Engineering Applications of Neural Networks. EANN 2016 - Aberdeen, UK, 2-5 September, 2016 Duration: 19 Aug 2016 → …

Publication series

Name	Communications in Computer and Information Sciences

Conference

Conference	Engineering Applications of Neural Networks. EANN 2016
Period	19/08/16 → …

Bibliographical note

The final publication is available at link.springer.com

Keywords

Two-hidden-layer feedforward
ANN
Exhaustive search
Optimal topology
Optimal node ratio
Heurix
Universal function approximation

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ThomasEANNFinal.pdfAccepted author manuscript, 209 KBLicence: Unspecified

Cite this

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title = "Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks",

abstract = "Two-hidden-layer feedforward neural networks are investigated for the existence of an optimal hidden node ratio. In the experiments, the heuristic n1 =int (0.5nh + 1), where n1 is the number of nodes in the first hidden layer and nh is the total number of hidden nodes, found networks with generalisation errors, on average, just 0.023%-0.056% greater than those found by exhaustive search. This reduced the complexity of an exhaustive search from quadratic, to linear in , with very little penalty. Further reductions in search complexity to logarithmic could be possible using existing methods developed by the Authors",

keywords = "Two-hidden-layer feedforward, ANN, Exhaustive search, Optimal topology, Optimal node ratio, Heurix, Universal function approximation",

author = "Alan Thomas and Simon Walters and Miltiadis Petridis and {Malekshahi Gheytassi}, Mohammad and Robert Morgan",

note = "The final publication is available at link.springer.com; Engineering Applications of Neural Networks. EANN 2016 ; Conference date: 19-08-2016",

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doi = "10.1007/978-3-319-44188-7_19",

language = "English",

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pages = "253--266",

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Thomas, A , Walters, S, Petridis, M, Malekshahi Gheytassi, M & Morgan, R 2016, Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks. in Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Sciences, Springer International Publishing, Switzerland, pp. 253-266, Engineering Applications of Neural Networks. EANN 2016, 19/08/16. https://doi.org/10.1007/978-3-319-44188-7_19

Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks. / Thomas, Alan ; Walters, Simon; Petridis, Miltiadis et al.
Engineering Applications of Neural Networks. EANN 2016. Switzerland: Springer International Publishing, 2016. p. 253-266 (Communications in Computer and Information Sciences).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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T1 - Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks

AU - Thomas, Alan

AU - Walters, Simon

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AU - Morgan, Robert

N1 - The final publication is available at link.springer.com

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N2 - Two-hidden-layer feedforward neural networks are investigated for the existence of an optimal hidden node ratio. In the experiments, the heuristic n1 =int (0.5nh + 1), where n1 is the number of nodes in the first hidden layer and nh is the total number of hidden nodes, found networks with generalisation errors, on average, just 0.023%-0.056% greater than those found by exhaustive search. This reduced the complexity of an exhaustive search from quadratic, to linear in , with very little penalty. Further reductions in search complexity to logarithmic could be possible using existing methods developed by the Authors

AB - Two-hidden-layer feedforward neural networks are investigated for the existence of an optimal hidden node ratio. In the experiments, the heuristic n1 =int (0.5nh + 1), where n1 is the number of nodes in the first hidden layer and nh is the total number of hidden nodes, found networks with generalisation errors, on average, just 0.023%-0.056% greater than those found by exhaustive search. This reduced the complexity of an exhaustive search from quadratic, to linear in , with very little penalty. Further reductions in search complexity to logarithmic could be possible using existing methods developed by the Authors

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KW - Universal function approximation

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DO - 10.1007/978-3-319-44188-7_19

M3 - Conference contribution with ISSN or ISBN

SN - 9783319441870

T3 - Communications in Computer and Information Sciences

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EP - 266

BT - Engineering Applications of Neural Networks. EANN 2016

PB - Springer International Publishing

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T2 - Engineering Applications of Neural Networks. EANN 2016

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ER -

Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this