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The Generalization Complexity Measure for Continuous Input Data
We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005069/ https://www.ncbi.nlm.nih.gov/pubmed/24983000 http://dx.doi.org/10.1155/2014/815156 |
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author | Gómez, Iván Cannas, Sergio A. Osenda, Omar Jerez, José M. Franco, Leonardo |
author_facet | Gómez, Iván Cannas, Sergio A. Osenda, Omar Jerez, José M. Franco, Leonardo |
author_sort | Gómez, Iván |
collection | PubMed |
description | We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets. |
format | Online Article Text |
id | pubmed-4005069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-40050692014-06-30 The Generalization Complexity Measure for Continuous Input Data Gómez, Iván Cannas, Sergio A. Osenda, Omar Jerez, José M. Franco, Leonardo ScientificWorldJournal Research Article We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets. Hindawi Publishing Corporation 2014 2014-04-10 /pmc/articles/PMC4005069/ /pubmed/24983000 http://dx.doi.org/10.1155/2014/815156 Text en Copyright © 2014 Iván Gómez et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Gómez, Iván Cannas, Sergio A. Osenda, Omar Jerez, José M. Franco, Leonardo The Generalization Complexity Measure for Continuous Input Data |
title | The Generalization Complexity Measure for
Continuous Input Data |
title_full | The Generalization Complexity Measure for
Continuous Input Data |
title_fullStr | The Generalization Complexity Measure for
Continuous Input Data |
title_full_unstemmed | The Generalization Complexity Measure for
Continuous Input Data |
title_short | The Generalization Complexity Measure for
Continuous Input Data |
title_sort | generalization complexity measure for
continuous input data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005069/ https://www.ncbi.nlm.nih.gov/pubmed/24983000 http://dx.doi.org/10.1155/2014/815156 |
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