dc.description.references | Bezdek J.C., Pal N.R. 1995. A note on self-organizing semantic maps. IEEE Trans. on Neural Networks, 6(5):1029-1036.
Blackmore J., and Miikkulainen R. 1993. Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map, Proc. ICNN’93, Int. Conf. on Neural Networks, Vol. I, 450-455, IEEE Service Center.
Bruske, J., and Sommer G. 1995. Dynamic cell structure learns perfectly topology preserving map. Neural Comp., 7, 845-865.
Deboeck, G. 1999. Investment maps for emerging markets. Neuro-fuzzy techniques for intelligent information systems (N.Kasabov and R.Kozma Eds.). Physica Verlag, 373-395.
Fritzke, B. 1991. Unsupervised clustering with growing cell structures. Proc. IJCNN 91, 531-536.
Fritzke, B. 1994. Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Networks, 7, 1441-1460.
Fritzke, B. 1995. A growing neural gas network learns topologies, in Advances in neural information processing Systems , D. Touretzky, T.K. Keen eds., pp.625-632, Cambridge MA: MIT Press.
Heinke, D., and Hamker, F.H. 1998. Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP. IEEE Trans. on Neural Networks, 9, 1279- 1291.
Heskes, T.M., and Kappen B. 1991. Learning processes in neural networks. Physical Review A, 44, 2718-2726.
Kadirkamanathan, V., Niranjan, M. 1993. A function estimation approach to sequential learning with neural networks, Neural Comp., 5, 954-975.
Kasabov, N. 1998a. The ECOS framework and the ECO learning method for evolving connectionist systems. Jour. of Advanced Computational Intelligence, 2, 1-8.
Kasabov, N. 1998b. Evolving fuzzy neural networks - algorithms, applications and biological moti- vation. in: Yamakawa and Matsumoto (Eds.) Methodologies for the Conception, Design, and Application of Soft Computing, World Scientific, 271-274.
Kasabov, N., Erzegoveri, L. et. al. 2000. Hybrid intelligent decision support systems and applications for risk analysis and prediction of evolving economic clusters in Europe. To appear in N.Kasabov (ed), Future Directions for Intelligent Systems and Information Sciences, Physica Verlag (Springer Verlag).
Kaski, S. 1997. Data exploration using self-organizing maps. Mathematics, Computing and Management in Engineering Series No. 82, Acta Polytechnica Scandinavica.
Kohonen T. 1982. Self-organizing formation of topologically correct feature maps, Biological Cybernetics, v. 43, 59-69.
Kohonen, T., Hynninen, J., Kangas, J. et al. 1996. LVQ PAK: The learning vector quantization program package, Report A30, Laboratory of Computer and Information Science, Helsinki University of Technology.
Kohonen, T. 1997. Self-Organizing Maps, second edition, Springer.
Lawrence, S., and Giles, C.L. 1998. Searching the world wide web, Science, Apr. 3, 1998, pp.98-100.
Mao J., and Jain, A.K. 1995. Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. on Neural Networks, 6, 296-317.
MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations, Proc. 5th Berkeley Symp. on Mathematics, L.M. LeCam and J. Neyman eds., pp.281-297.
Martinetz T.M., Berkovich S.G. and Schulten K.J. 1993. “Neural-Gas” network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks, 4, 558-569.
Meyering, A. and Ritter H. 1992. Learning 3d-shape-perception with local linear maps, in Proc. of IJCNN 92, pp.IV:432-436, Baltimore.
Mulier, F. and Cherkassky, V. 1995. Self-organization as an interative kernel smoothing process, Neural Comp., 7, 1165-1177.
Nowlan, S.J. 1990. Maximum likelihood competitive learning, in Advances in Neural Information Processing Systems 2, D. Touretzky ed., pp.574-582, New York: Morgan Kauffman.
Platt, J. 1991. A resource-allocating network for function interpolation. Neural Comp., 3, 213-225.
Ritter H., Kohonen T. 1989. Self-organizing semantic maps, Biological Cybernetics, 61, 241-254.
Robinson A.J. 1989. Dynamic error propagation networks. Ph.D. thesis, Cambridge University.
Rosipal R., Koska M., and Farkas, I. 1998. Prediction of chaotic time-series with a resource-allocating RBF network. Neural Processing Letters, 7, 185-197.
Sammon, Jr., J. 1969. A non-linear mapping for data structure analysis. IEEE Trans. on Comput- ers, 18, 401-09.
Serrano-Cinca, C. 1996. Self organizing neural networks for financial diagnosis. Decision Support Systems, 17, 227-38.
Schaal, S., & Atkeson, C.G. 1998. Constructive incremental learning from only local information. Neural Comp., 10, 2047-2084.
Vesanto, J. 1997. Using the SOM and local models in time-series prediction. Proc. of WSOM’97, pp.209-214. Helsinki University of Technology, Neural Networks Research Centre, Finland. | en_NZ |