TY - JOUR 
A1 - García Laencina, Pedro José
AU - Sancho Gómez, José Luis
T1 - Red neural multitarea para problemas de decisión con información incompleta
Y1 - 2006
UR - http://hdl.handle.net/10317/1354
AB - Missing data is a common problem that appears
in many real applications. Handling missing data is a essential
requirement for pattern classification because inappropriate
treatment of missing data may cause large errors or false results
on classification. A classical approach is to estimate and fill
the missing values. Up to now, proposed methods are efficient
but they do not focus the missing data estimation to pattern
classification. In this work, we propose a novel neural network
that uses the advantages of Multitask Learning (MTL) to classify
patterns with incomplete data. A MTL neural network learns
at the same time the classification task and the different task
associated to incomplete features. Missing data estimation is
guided and oriented by the classification task during the MTL
process. Obtained results based on real and artificial classification
problems demonstrate the advantages of the proposed algorithm.
KW - Ingeniería Telemática
KW - Red neural multitarea
KW - Aprendizaje multitarea
KW - Clasificación de patrones
KW - Información incompleta
KW - Red neuronal artificial
KW - Neural network multitask
KW - Multitask learning
KW - Pattern classification
KW - Incomplete information
KW - Artificial neural networks
LA - spa
PB - Universidad de Oviedo. Escuela Politécnica Superior de Ingeniería (Gijón)
ER -