Aprendizaje multitarea en problemas con un número reducido de datos
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URI: http://hdl.handle.net/10317/706Share
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Teoría de la Señal y las ComunicacionesSponsors
Este trabajo está subvencionado por el Ministerio de Educación y Ciencia, otorgado por TIC2002-03033.Publication date
2005-09Publisher
Universidad Politécnica de ValenciaBibliographic Citation
BUENO CRESPO, Andrés y SANCHO GÓMEZ, José Luis. Aprendizaje multitarea en problemas con un número reducido de datos. En: Simposium Nacional de la Unión Científica Internacional de Radio (20º: 2005: Gandía) XX Simposium Nacional de URSI 2005. URSI 05, Gandía del 14-16 de Septiembre, 2005. Valencia: Universidad Politécnica de Valencia, 2005.Keywords
Aprendizaje multitareaRed Neuronal Artificial (RNA)
Tarea artificial
Edición de datos
Learning multitasking
Artificial neural networks
Artificial task
Data editing
Abstract
MultiTask Learning (MTL) is a procedure to
train a neural network to learn several related tasks
simultaneously considering one of them as main task and
the others as secondary tasks. In this paper, we have
tested a method to obtain artificially tasks which are
related with the main one, because in many real cases,
knowledge about problem to be solved is uncertain.
We use sample selection techniques to generate related
tasks with the main one, in particular, samples close the
classification boundary. Moreover, a new procedure to
train MultiLayer Perceptrons with generated tasks is
described.
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