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Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Automatic Design of Neural Network Structures Using AiS

Authors : Toshisada Mariyama, Kunihiko Fukushima, Wataru Matsumoto

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Structures of neural networks are usually designed by experts to fit target problems. This study proposes a method to automate small network design for a regression problem based on the Add-if-Silent (AiS) function used in the neocognitron. Because the original AiS is designed for image pattern recognition, this study modifies the intermediate function to be Radial Basis Function (RBF). This study shows that the proposed method can determine an optimized network structure using the Bike Sharing Dataset as one case study. The generalization performance is also shown.

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Literature
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Metadata
Title
Automatic Design of Neural Network Structures Using AiS
Authors
Toshisada Mariyama
Kunihiko Fukushima
Wataru Matsumoto
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_32

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