A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity-Reference-Cited by-同舟云学术

A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity

Published:2015 Issue: Volume: Page:13-27
ISSN:1431-8814
Container-title:Data Science, Learning by Latent Structures, and Knowledge Discovery
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Author:

Akbilgic Oguz,Bozdogan Hamparsum

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-662-44983-7_2

Reference26 articles.

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. H. Petrox & F. Csaki, (Eds.), Second International Symposium on Information Theory (pp. 267–281). Budapest: Academiai Kiado.

2. Akbilgic, O. (2011). Variable selection and prediction using hybrid radial basis function neural networks: A case study on stock markets. PhD thesis, Istanbul University.

3. Akbilgic, O., & Bozdogan, H. (2011). Predictive subset selection using regression trees and rbf neural networks hybridized with the genetic algorithm. European Journal of Pure and Applied Mathematics, 4(4), 467–485.

4. Akbilgic, O., Bozdogan, H., & Balaban, M. E. (2013). A novel hybrid RBF neural network model as a forecaster. Statistics and Computing. doi:10.1007/s11222-013-9375-7.

5. Anderson, R. (2007). The credit scoring toolkit. Oxford: Oxford University Press.

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