Some experiments on training radial basis functions by gradient descent

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3316, Issue , 2004, Pages 428-433

  Fernández Redondo, Mercedes ^a   Hernández Espinosa, Carlos ^a   Ortiz Gómez, Mamen ^a   Torres Sospedra, Joaquín ^a  

^a UNIVERSITAT JAUME I   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUNCTIONS; ONLINE SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

GRADIENT DESCENT; NUMBER OF ITERATIONS; RADIAL BASIS FUNCTION(RBF); RADIAL BASIS FUNCTIONS; SPEED OF CONVERGENCE; SUPERVISED TRAININGS; TRAINING ALGORITHMS; UNSUPERVISED TRAINING;

E-LEARNING;

EID: 35048829891     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30499-9_65     Document Type: Article

References (6)

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3. Hwang, Y., Bang, S., "An Efficient Method to Construct a Radial Basis Function Neural Network Classifier". Neural Network. 10 no. 8, (1997) 1495-1503
4. Roy, A., Govil, S., et alt., "An Algorithm to Generate Radial Basis Function (RBF)-LikeNets for Classification Problems". Neural Networks. 8 no. 2, (1995) 179-201
5. Krayiannis, N., "Reformulated Radial Basis Neural Networks Trained by Gradient Descent". IEEE Trans. on Neural Networks. 10 no. 3, (1999) 657-671
6. Krayiannis, N., Randolph-Gips, M., "On the Construction and Training of Reformulated Radial Basis Functions". IEEE Trans. Neural Networks. 14 no. 4, (2003) 835-846