Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems
DOI:
https://doi.org/10.18201/ijisae.267039Keywords:
Machine learning, Modified translated multiplicative neuron model, Monk’s and Balloon problems, N-bit parity problem, Translated multiplicative neuron modelAbstract
In this study, solutions to machine learning problems such as Monk’s 2 (M2), Balloon and Tic-Tac-Toe problems employing a single neuron dependent on rules which use either modified translated multiplicative (πm) neuron or McCulloch-Pitts neuron model is proposed. Since M2 problem is similar to N-bit parity problem, translated multiplicative (πt) neuron model is modified for M2 problem. Also, McCulloch-Pitts neuron model is used to increase classification performance. Then either πm or McCulloch-Pitts neuron model is applied to Balloon and Tic-Tac-Toe problems. When the result of proposed only one πm neuron model that is not required any training stage and hidden layer is compared with the other approaches, it shows satisfactory performance.Downloads
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Iyoda, E. M., Nobuhara, H. and Hirota, K.: A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron, Neural Processing Letters, vol.18, pp. 213-218, 2003.
Arslanov, M.Z., Ashigaliev, D.U. and Ismail, E. E.: N-bit Parity Ordered Neural Network, Neurocomputing 48 (2002), 1053-1056
Al-Rawi, M.: A Neural Network to Solve the Hybrid N-parity: Learning with Generalization Issues, Neurocomputing, vol.68, pp. 273-280, 2005
Hohil, M. E., Liu, D., Smith, S. H., Solving the N-bit parity problem using neural networks, Neural Networks, vol.12, pp.1321-1323, 1999.
Li, D., Hirasawa, K., Hu, J., Murata, J., Studying the effects on multiplication neurons for parity problem, 41st Society of Instrument and Control Engineers-SICE,2002.
Kim, K., Kim, S., Joo, Y., Oh, A.S.: Enhanced fuzzy single layer perceptron, Advances in Neural Networks, vol.3496,pp. 603-608, 2005.
Setino, R.: On the solution of the parity problem by a single hidden layer feedforward neural network, Neurocomputing, vol.16 (3), pp. 225-235, 1997.
Setiono, R., Hui, L. C. K.: Some N-bit parity problems are solvable by feed-forward networks with less than n hidden units, Int. Joint Conf. on Neural Networks, 1993, pp. 305-308.
Schmitt, M.: On the complexity of computing and learning with multiplicative neurons, Neural Computation, vol.14(2), pp. 241-301, 2002.
Zhang, B.-T.: A Bayesian Evolutionary Approach to The Design and Learning of Heterogeneous Neural Trees, Integrated Computer-Aided Engineering, vol. 9(1), pp. 73-86, 2002.
Bas, E., Uslu, V. R. and Egrioglu, E.: Robust learning algorithm for multiplicative neuron model artificial neural networks, Expert Systems with Applications, vol.56, pp. 80-88, 2016.
Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzeroski, S., Fahlman, S.E., Fisher, D., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski, R.S., Mitchell, T., Pachowicz, P., Reich, Y., Vafaie, H., Van de Welde, W., Wenzel, W., Wnek, J., and Zhang, J.: The Monk’s Problems: A Perfor. Comparison of Different Learning Algorithm, a Report, Carnegie Mellon University CMU-CS-91-197, 1991.
University of California, Irvine Dataset: [Online]. Available: ftp://ftp.ics.uci.edu/pub/machine-learning-databases/monks-problems/, retrieved November 16 2016.
Pilgrim, R., A., “Tic-Tac-Toe: Introduction Expert Systems to Middle School Students”, Acm Sigcse Bulletin, Vol. 27, 340–344, (1995).
Gordon, A., “A General Algorithm for Tic-Tac-Toe Board Evaluation”, Journal of Computing Sciences in Colleges, Vol. 21, 42-46, (2006).
Solorio, T. and Fuentes, O.: Taking Advantage of Unlabelled Data with the Ordered Classification Algorithm, ACTA, Proc. of AI and Soft Computing ASC 2002, 357-200, (2002).
Noughts And Crosses - The oldest graphical computer game, http://www.pong-story.com/1952.htm, retrieved November 16, 2016.
Wachsmuth, B. G., Tic-tac-toe game, http://pirate.shu.edu/~wachsmut/Teaching/CSAS1111/Assigns-CPP/assign7.html, retrieved November 17, 2016.
Massey, B., Tic-tac-toe board evaluation, http://web.cecs.pdx.edu/~bart/cs541-fall2001/homework/3-learn.html, retrieved November 17, 2016.
Appel, A. W., Game player programs, http://www.cs.princeton.edu/courses/archive/spr05/cos217/asgts/gameplayer/, retrieved November 16, 2016.
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