Abstract
This paper presents the initial results concerning a new Radial Basis Function Artificial Neural Network (RBFNN) architecture for pattern classification. Performance of the new architecture is demonstrated with different data sets. Its efficiency is also compared with different classification methods reported in literature: Multilayer Perceptron, Standard Radial Basis Neural Networks, KNN and Minimum Distance classifiers, showing a much better performance. Results are only given for problems using two features
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Keywords
- Radial Basis Function
- Training Procedure
- Radial Basis Function Neural Network
- Decision Function
- Radial Basis Function Network
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References
Sanz, G.M., de la Cruz García, J.M.: Visión por computadora Imágenes digitales y aplicaciones, 2nd edn. Editorial Alfaomega (2008)
Cruz, P.P.: Inteligencia Artificial con Aplicaciones a la Ingeniería. Editorial Alfaomega (2010)
Ritter, G.X., Laurentiu, I., Urcid, G.: Morphological perceptron with dendritic structure. In: The 12th IEEE International Conference on Fuzzy System, pp. 1296–1301 (2003)
Ritter, G.X., Sussner, P.: Morphological perceptrons. Intelligent System and Semiotics, pp. 221–226 (1997)
Ritter, G.X., Urcid, G.: Lattice Algebra Approach to Single-Nueron Computation. IEEE Transactions on Neural Networks 14(2), 282–295 (2003)
Skomorokhov, A.: Radial Basis Function Networks in A+. In: Proceedings of the Conference on APL 2002, vol. 32(4), pp. 198–213 (2002)
Sug, H.: An Empirical Improvement of the Accuracy of RBF Networks. In: Proceedings of the Second International Conference on Interaction Sciences, ICIS 2009, pp. 708–712 (2009)
Bishop, C.M.: Neural Network for Pattern Reconigtion, 1st edn. Oxford University Press, New York (2008)
Liu, J., Lampinen, J.: A Differential Evolution Based Incremental Training Method for RBF Networks. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 881–888 (2005)
Pandya, A.S., Macy, R.B.: Pattern Recognition With Neural Networks in C++. IEEE Press (1995)
Principe, J.C., Euliano, N.R., Curt Lef, W.: Neural and Adaptive Systems Fundamentals Through Simulations. In: Library of Congress Cataloging-in-Publication Data (2000)
Sossa, H., Guevara, E.: Efficient Training for dendrite morphological neural networks. Neurocomputing 131, 132–142 (2014)
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Sossa, H., Cortés, G., Guevara, E. (2014). New Radial Basis Function Neural Network Architecture for Pattern Classification: First Results. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_86
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DOI: https://doi.org/10.1007/978-3-319-12568-8_86
Publisher Name: Springer, Cham
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