TY - GEN
T1 - Automatic construction of radial-basis function networks through an adaptive partition algorithm
AU - Ocampo-Vega, Ricardo
AU - Sanchez-Ante, Gildardo
AU - Falcon-Morales, Luis E.
AU - Sossa, Humberto
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Radial-Basis Function Neural Networks (RBFN) are a well known formulation to solve classification problems. In this approach, a feedforward neural network is built, with one input layer, one hidden layer and one output layer. The processing is performed in the hidden and output layers. To adjust the network for any given problem, certain parameters have to be set. The parameters are: the centers of the radial functions associated to the hidden layer and the weights of the connections to the output layer. Most of the methods either require a lot of experimentation or may demand a lot of computational time. In this paper we present a novel method based on a partition algorithm to automatically compute the amount and location of the centers of the radial-basis functions. Our results, obtained by running it in seven public databases, are comparable and even better than some other approaches.
AB - Radial-Basis Function Neural Networks (RBFN) are a well known formulation to solve classification problems. In this approach, a feedforward neural network is built, with one input layer, one hidden layer and one output layer. The processing is performed in the hidden and output layers. To adjust the network for any given problem, certain parameters have to be set. The parameters are: the centers of the radial functions associated to the hidden layer and the weights of the connections to the output layer. Most of the methods either require a lot of experimentation or may demand a lot of computational time. In this paper we present a novel method based on a partition algorithm to automatically compute the amount and location of the centers of the radial-basis functions. Our results, obtained by running it in seven public databases, are comparable and even better than some other approaches.
KW - Adaptive parameter adjustment
KW - Classification
KW - Neural networks
KW - Radial-basis functions
UR - http://www.scopus.com/inward/record.url?scp=84977553572&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-39393-3_20
DO - 10.1007/978-3-319-39393-3_20
M3 - Contribución a la conferencia
SN - 9783319393926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 198
EP - 207
BT - Pattern Recognition - 8th Mexican Conference, MCPR 2016, Proceedings
A2 - Olvera-López, José Arturo
A2 - Martínez-Trinidad, José Francisco
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Ayala-Ramírez, Víctor
A2 - Jiang, Xiaoyi
PB - Springer Verlag
T2 - 8th Mexican Conference on Pattern Recognition, MCPR 2016
Y2 - 22 June 2016 through 25 June 2016
ER -