Abstract
The paper presents the design of municipal creditworthiness parameters. Further, a model is designed based on Learning Vector Quantization neural networks for municipal creditworthiness classification. The model is composed of Kohonen’s Self-organizing Feature Maps (unsupervised learning) whose outputs represent the input of the Learning Vector Quantization neural networks (supervised learning).
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References
Olej, V., Hájek, P.: Modelling of Municipal Rating by Unsupervised Methods. In: WSEAS Transactions on Systems, vol. 6(7), pp. 1679–1686. WSEAS Press (2006)
Olej, V., Hájek, P.: Hierarchical Structure of Fuzzy Inference Systems Design for Municipal Creditworthiness Modelling. In: WSEAS Transactions on Systems and Control, vol. 2(2), pp. 162–169. WSEAS Press (2007)
Hájek, P., Olej, V.: Municipal Creditworthiness Modelling by means of Fuzzy Inference Systems and Neural Networks. In: 4th International Conference on Information Systems and Technology Management, TECSI-FEA USP, Sao Paulo, Brazil, pp. 586–608 (2007)
Hájek, P.: Municipal Creditworthiness Modelling by Computational Intelligence Methods. Ph.D. Thesis, University of Pardubice (2006)
Mead, D.M.: Assessing the Financial Condition of Public School Districts. Selected Papers in School Finance, National Center for Education Statistics, Washington DC (2001)
Mercer, T.A.: Financial Condition Index for Nova Scotia Municipalities. Government Finance Review 12(5), 36–39 (1996)
Miller, G.J.: Handbook of Debt Management. Marcel Dekker, New York (2003)
Serve, S.: Assessment of Local Financial Risk: The Determinants of the Rating of European Local Authorities-An Empirical Study Over the Period 1995-1998. In: EFMA Lugano Meetings, Lugano (2001)
Ammar, S., Duncombe, W., Hou, Y., Jump, B., Wright, R.H.: Using Fuzzy Rule-Based Systems to Evaluate Overall Financial Performance of Governments: An Enhancement to the Bond Rating Process. Public Budgeting and Finance 21(4), 91–110 (2001)
Haykin, S.S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Upper Saddle River (1999)
Kvasnička, V., et al.: Introduction to Neural Networks. Iris, Bratislava (1997) (in Slovak)
Kohonen, T.: Self-Organizing Maps. Springer, New York (2001)
Carpenter, G.A., Grossberg, S., Reynolds, J.H.: ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-organizing Neural Network. Neural Networks 4(5), 565–588 (1991)
Speckt, D.F.: Probabilistic Neural Networks. Neural Networks 3(1), 109–118 (1990)
Cristianini, N., Shawe-Taylor, J.: Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Cambridge (2006)
Hájek, P., Olej, V.: Municipal Creditworthiness Modelling by Clustering Methods. In: Margaritis, Illiadis (eds.) 10th International Conference on Engineering Applications of Neural Networks, EANN 2007, Thessaloniky, Greece, pp. 168–177 (2007)
Stein, B.: Meyer zu Eissen, S., Wissbrock, F.: On Cluster Validity and the Information Need of Users. In: International Conference on Artificial Intelligence and Applications (AIA 2003), Benalmádena, Spain, pp. 216–221 (2003)
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Hájek, P., Olej, V. (2008). Municipal Creditworthiness Modelling by Kohonen’s Self-organizing Feature Maps and LVQ Neural Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_6
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DOI: https://doi.org/10.1007/978-3-540-69731-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
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