Abstract
C-Mantec is a recently introduced constructive algorithm that generates compact neural architectures with good generalization abilities. Nevertheless, it produces a discrete output value and this might be a drawback in certain situations. We propose in this work two approaches in order to obtain a continuous output network such as the output can be interpreted as the probability of a given pattern to belong to one of the output classes. The CC-Mantec approach utilizes a committee strategy and the results obtained both with the XOR Boolean function and with a set of benchmark functions shows the suitability of the approach, as an improvement over the standard C-Mantec algorithm is obtained in almost all cases.
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References
Baum, E.B., Haussler, D.: What size net gives valid generalization? Neural Comput. 1(1), 151–160 (1989)
Frean, M.: A thermal perceptron learning rule. Neural Comput. 4(6), 946–957 (1992)
Gómez, I., Franco, L., Jerez, J.M.: Neural network architecture selection: can function complexity help? Neural Process. Lett. 30(2), 71–87 (2009)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)
do Carmo Nicoletti, M., Bertini Jr., J.R.: An empirical evaluation of constructive neural network algorithms in classification tasks. Int. J. Innov. Comput. Appl. 1(1), 2–13 (2007)
Perrone, M.P., Cooper, L.N.: When networks disagree: Ensemble methods for hybrid neural networks, pp. 126–142. Chapman and Hall (1993)
Subirats, J.L., Franco, L., Jerez, J.M.: C-mantec: A novel constructive neural network algorithm incorporating competition between neurons. Neural Netw. 26, 130–140 (2012)
Subirats, J.L., Jerez, J.M., Franco, L.: A new decomposition algorithm for threshold synthesis and generalization of boolean functions. IEEE Trans. on Circuits and Systems 55-I(10), 3188–3196 (2008)
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Subirats, J.L., Luque-Baena, R.M., Urda, D., Ortega-Zamorano, F., Jerez, J.M., Franco, L. (2013). Committee C-Mantec: A Probabilistic Constructive Neural Network. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_33
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DOI: https://doi.org/10.1007/978-3-642-38679-4_33
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