Inductive Modelling World Wide the State of the Art. Miroslav Snorek, Pavel Kordik. IWIM, Prague, 2007.
Article (in pdf)
Abstract. In this contribution, we summarize the state of the art of the inductive modelling world wide. Recently, there is a trend to utilize evolutionary algorithms for optimization of inductive models. Also some new approaches in feature selection using inductive modelling, model validation, ensembles of inductive models, etc. are to be described.
Keywords. Inductive modeling, state of the art.
References.
- Ivakhnenko A. G.: Polynomial theory of complex systems. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(1):364378, 1971.
- A. Barron and R. Baron, “Statistical learning networks: A unifying. view,” in Proc. 20th Symp. Interface, 1988, pp. 192-203.
- P. Kordik. Fully Automated Knowledge Extraction using Group of Adaptive Models Evolution. PhD thesis, Dep. of Comp. Sci. and Computers, FEE, CTU Prague, Czech Republic, September 2006.
- Oh, S.; Pedrycz, W. & Park, B. (2003), 'Polynomial neural networks architecture: analysis and design', Computers and Electrical Engineering 29, 703-725.
- Oh, S.K. & Pedrycz, W. (2002), 'The design of self-organizing Polynomial Neural Networks', Inf. Sci. 141, 237-258.
- Oh, S. & Pedrycz, W. (2005), 'A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization', Physics Letters A(345), 88-100.
- Nariman-Zadeh, N.; Darvizeh, A.; Jamali, A. & Moeini, A. (2005), 'Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process', Journal of Materials Processing Technology(165), 1561-1571.
- Nikolaev, N.Y. & Iba, H. (2003), 'Polynomial harmonic GMDH learning networks for time series modeling', Neural Networks(16), 1527-1540.
- Gilbar & Thomas, C. (2002),'A new GMDH type algorithm for the development of neural networks for pattern recognition', PhD thesis, FLORIDA ATLANTIC UNIVERSITY.
- Schetinin, V. (2003), 'A Learning Algorithm for Evolving Cascade Neural Networks', Neural Processing Letters(17), 21-31.
- Kondo, T.; Ueno, J. & Kondo, K. (2005), 'Revised GMDH-Type Neural Networks Using AIC or PSS Criterion and Their Application to Medical Image Recognition', Journal of Advanced Computational Intelligence and Intelligent Informatics 9(3), 257-267.
- Abdel-Aal, R. (2005), 'GMDH-based feature ranking and selection for improved classification of medical data', Journal of Biomedical Informatics 38, 456-468.
- Madala, H.R. & Ivakhnenko, A.Ration, B., ed. (1994), Inductive Learning Algorithm for Complex System Modelling, CRC Press.
- Kurkova, V. (1991), 'Kolmogorov's Theorem Is Relevant', Neural Computation 3, 617-622.
- Koutník, J. (2004), 'Modular Neural networks for Analysis and Recognition of Real Data'(DCPSR-2004-08), Technical report, Czech Technical University in Prague, FEE, CTU Prague, Czech Republic.
- Mandischer, M. (2002), 'A comparison of evolution strategies and backpropagation for neural network training', Neurocomputing(42), 87-117.
- Sexton, R.S. & Gupta, J.N.D. (2000), 'Comparative evaluation of genetic algorithm and backpropagation for training neural networks', Information Sciences(129), 45-59.
- Fahlman, S.E. & Lebiere, C. (1991),'The Cascade-Correlation Learning Architecture'(CMUCS-90-100), Technical report, Carnegie Mellon University Pittsburgh, USA.
- Wickera, D.; Rizkib, M.M. & Tamburinoa, L.A. (2002), 'E-Net:Evolutionary neural network synthesis', Neurocomputing 42, 171-196.
- Schetinin, V. (2003), 'A Learning Algorithm for Evolving Cascade Neural Networks', Neural Processing Letters(17), 21-31.
- Stanley, K.O. & Miikkulainen, R. (2002),Continual Coevolution Through Complexification., in 'GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002', pp. 113-120.
- Stanley, K.O. & Miikkulainen, R. (2002), 'Evolving Neural Networks through Augmenting Topologies', Evolutionary Computation 10(2), 99-127.
- Stanley, K.; Bryant, B. & Miikkulainen, R. (2005), 'Real-time neuroevolution in the NERO video game', Evolutionary Computation, IEEE Transactions on 9(6), 653-668.
- Mahfoud, S.W. (1995),'Niching Methods for Genetic Algorithms'(95001), Technical report, Illinois Genetic Algorithms Laboratory (IlliGaL), University of Ilinios at Urbana-Champaign.
- Juille, H. & Pollack, J.B. (1996),Co-evolving intertwined spirals, in Lawrence J. Fogel, Peter J. Angeline & T. Baeck, ed.,'Proceedings of the Fifth Annual Conference on Evolutionary Programming', MIT Press, , pp. 461-467.
- Brown, G.: Diversity in Neural Network Ensembles, PhD thesis, The University of Birmingham, 2004
- Ivakhnenko, A.G., Müller, J.-A.: Self-organisation of nets of active neurons. SAMS, vol.20, no.1-2, 1995, pp.93-106.
- Ivakhnenko, A.G.; Savchenko, E.A. & Ivakhnenko, G.A.: Problems of future GMDH algorithms development, Systems Analysis Modelling Simulation 43, p. 1301 – 1309, 2003.
- Ivakhnenko,A.G., Ivakhnenko,G.A. and Muller,J.A., Self-Organization of Neural Networks with Active Neurons. Pattern Recognition and Image Analysis, 1994, vol.4, no.2, pp.185-196.
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