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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. doi: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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Title: F-Measure as the error function to train neural networks
Author: Pastor Pellicer, Joan Zamora Martínez, Francisco Julián España Boquera, Salvador Castro-Bleda, Maria Jose
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
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 ...[+]
Subjects: Neural Networks , Error-Backpropagation algorithm , F-Measure , Imbalanced datasets
Copyrigths: Cerrado
ISBN: 978-3-642-38678-7
Source:
Advances in Computational Intelligence. (issn: 0302-9743 )
DOI: 10.1007/978-3-642-38679-4_37
Publisher:
Springer Verlag (Germany)
Publisher version: http://link.springer.com/chapter/10.1007/978-3-642-38679-4_37
Series: Lecture Notes in Computer Science;
Project ID:
MICINN/TIN2010-18958
MICINN/FPI/BES-2011-046167
Thanks:
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.
Type: Capítulo de libro

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