Title :
A Multi-objective Learning Algorithm for RBF Neural Network
Author :
Kokshenev, Illya ; Braga, Antonio Padua
Author_Institution :
Depto. Eng. Eletron., Univ. Fed. de Minas Gerais, Belo Horizonte
Abstract :
In this paper, the problem of multi-objective supervised learning is discussed within the non-evolutionary optimization framework. The proposed MOBJ learning algorithm performs the search of Pareto-optimal models determining weights,width, prototype vectors, and the quantity of basis functions of the RBF network. In combination with the Akaike information criterion, the algorithm provides high quality solutions.
Keywords :
Pareto optimisation; learning (artificial intelligence); radial basis function networks; search problems; Akaike information criterion; Pareto-optimal search; RBF neural network; multiobjective supervised learning algorithm; nonevolutionary optimization framework; Machine learning; Machine learning algorithms; Minimization methods; Neural networks; Optimization methods; Prototypes; Radial basis function networks; Risk management; Statistical learning; Supervised learning; LASSO; generalization; multi-objective learning; radial basis functions; regularization;
Conference_Titel :
Neural Networks, 2008. SBRN '08. 10th Brazilian Symposium on
Conference_Location :
Salvador
Print_ISBN :
978-1-4244-3219-6
Electronic_ISBN :
1522-4899
DOI :
10.1109/SBRN.2008.39
