Abstract
The paper presents the modelling possibilities of kernel-based approaches on a complex real-world problem, i.e. municipal creditworthiness classification. A model design includes data pre-processing, labelling of individual parameters’ vectors using expert knowledge, and the design of various support vector machines with supervised learning and kernel-based approaches with semi-supervised learning.
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Hajek, P., Olej, V. (2009). Municipal Creditworthiness Modelling by Kernel-Based Approaches with Supervised and Semi-supervised Learning. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_4
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DOI: https://doi.org/10.1007/978-3-642-03969-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03968-3
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