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F-Measure as the error function to train neural networks

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F-Measure as the error function to train neural networks

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Pastor Pellicer, J.; Zamora Martínez, FJ.; España Boquera, S.; Castro-Bleda, MJ. (2013). F-Measure as the error function to train neural networks. En Advances in Computational Intelligence. Springer Verlag (Germany). 376-384. https://doi.org/10.1007/978-3-642-38679-4_37

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/40169

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Metadatos del ítem

Título: F-Measure as the error function to train neural networks
Autor: Pastor Pellicer, Joan Zamora Martínez, Francisco Julián España Boquera, Salvador Castro-Bleda, Maria Jose
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
Imbalance datasets impose serious problems in machine learning. For many tasks characterized by imbalanced data, the F-Measure seems more appropiate than the Mean Square Error or other error measures. This paper studies ...[+]
Palabras clave: Neural Networks , Error-Backpropagation algorithm , F-Measure , Imbalanced datasets
Derechos de uso: Cerrado
ISBN: 978-3-642-38678-7
Fuente:
Advances in Computational Intelligence. (issn: 0302-9743 )
DOI: 10.1007/978-3-642-38679-4_37
Editorial:
Springer Verlag (Germany)
Versión del editor: http://link.springer.com/chapter/10.1007/978-3-642-38679-4_37
Serie: Lecture Notes in Computer Science;
Código del Proyecto:
info:eu-repo/grantAgreement/MICINN//TIN2010-18958/ES/HITITA: HERRAMIENTA INTERACTIVA PARA LA TRANSCRIPCION DE IMAGENES DE TEXTOS ANTIGUOS/
info:eu-repo/grantAgreement/MICINN//BES-2011-046167/ES/BES-2011-046167/
Agradecimientos:
This work has been partially supported by MICINN project HITITA (TIN2010-18958) and by the FPI-MICINN (BES-2011-046167) scholarship from Ministerio de Ciencia e Innovación, Gobierno de España.
Tipo: Capítulo de libro

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