Abstract
An n-class problem is decomposed into n two-class problems. Naturally, modular multilayer perceptrons (MLPs) come into being. A single- output MLP is behalf of a class and trained by a two-class learning subset. A training subset only consists of all samples from a special class and a part samples from the nearest classes. If the decision boundary of a single-output MLP is open, its outputs are amended by a correction coefficient. This paper clarifies such a fact that the generalization of a single-output MLP is seriously affected by the sample disequilibrium situation. Therefore, the samples from the little class have to be multiplied an enlarging factor. The result of letter recognition shows that the above methods are effective.
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References
Setiono, R.: Feedforward neural network construction using cross-validation. Neural Computation 13, 2865–2877 (2001)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Gormann, R.P., et al.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1, 75–89 (1988)
Cho, S.B.: Neural-network classifiers for recognizing totally unconstrained handwritten numerals. IEEE Transactions on Neural Networks 8(1), 43–52 (1997)
Daqi, G., Genxing, Y.: Influences of variable scales and activation functions on the performances of multilayer feedforward neural networks. Pattern Recognition 36(4), 869–878 (2003)
Williamson, J.R.: Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Networks 9(5), 881–897 (1996)
Frey, P.W., Slate, D.J.: Letter recognition using Holland-style adaptive classifiers. Machine Learning 6, 161–182 (1991)
Fogarty, T.: First nearest neighbor classification on Frey and Slate’s letter recognition problem. Machine Learning 9, 387–388 (1992)
Bressan, M., Guillamet, D., Vitria, J.: Using an ICA representation of high dimensional data for object recognition and classification. In: IEEE Conference on CVPR, HI, USA, vol. 1, pp. 1004–1009 (2001)
Islam, M.M., Murase, K.: A new algorithm to design compact two-hidden-layer artificial neural networks. Neural Networks 14, 1265–1274 (2001)
Anand, R., Mehrotra, K.G., Mohan, C.K., et al.: Efficient classification for multiclass problems using modular neural networks. IEEE Transactions on Neural Networks 6(1), 117–124 (1995)
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© 2005 Springer-Verlag Berlin Heidelberg
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Daqi, G., Zhu, S., Gu, W. (2005). A Modular Single-Hidden-Layer Perceptron for Letter Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_72
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DOI: https://doi.org/10.1007/11550822_72
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