[1] Akbani, R., Kwek, S., and Japkowicz, N. Applying support vector machines to imbalanced datasets. In Machine Learning: ECML 2004. Springer, 2004, pp. 39-50.

[2] Bache, K., and Lichman, M. Iris data set, 2013. Available at

[3] Bache, K., and Lichman, M. Statlog (australian credit approval) data set, 2013. Available at

[4] Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., and Vanthienen, J. Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54, 6 (2003), 627-635.

[5] Bellotti, T., and Crook, J. Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications 36, 2 (2009), 3302-3308.

[6] Berger, S., and Gleisner, F. Emergence of financial intermediaries in electronic markets: The case of online p2p lending. BuR Business Research Journal 2, 1 (2009).

[7] Brown, I., and Mues, C. An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications 39, 3 (2012), 3446-3453.

[8] Canals-Cerdá, J. J., and Kerr, S. Forecasting credit card portfolio losses in the great recession: a study in model risk. Tech. rep., 2014.

[9] Chang, C.-C., and Lin, C.-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2 (2011), 27:1-27:27. Software available at

[10] Chen, Y.-W., and Lin, C.-J. Combining svms with various feature selection strategies. In Feature Extraction. Springer, 2006, pp. 315-324.

[11] Ciresan, D., Meier, U., and Schmidhuber, J. Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (2012), IEEE, pp. 3642-3649.

[12] Cortes, C., and Vapnik, V. Support-vector networks. Machine learning 20, 3 (1995), 273-297.

[13] Cover, T. M. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. Electronic Computers, IEEE Transactions on, 3 (1965), 326-334.

[14] Cristianini, N., and Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.

[15] Cronin, S. Modern analytics: A look at the smo. part 1 of a week long series., September 2010.

[16] Crook, J. N., Edelman, D. B., and Thomas, L. C. Recent developments in consumer credit risk assessment. European Journal of Operational Research 183, 3 (2007), 1447-1465.

[17] de Souza, C. R. Kernel functions for machine learning applications, March 2010.

[18] DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics (1988), 837-845.

[19] Dumais, S., Platt, J., Heckerman, D., and Sahami, M. Inductive learning algorithms and representations for text categorization. In Proceedings of the seventh international conference on Information and knowledge management (1998), ACM, pp. 148-155.

[20] Elizondo, D. The linear separability problem: Some testing methods. Neural Networks, IEEE Transactions on 17, 2 (2006), 330-344.

[21] Engelmann, B., Hayden, E., and Tasche, D. Measuring the discriminative power of rating systems. Tech. rep., Discussion paper, Series 2: Banking and Financial Supervision, 2003.

[22] Finlay, S. Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior. Palgrave Macmillan, 2012.

[23] Fisher, R. A. The use of multiple measurements in taxonomic problems. Annals of eugenics 7, 2 (1936), 179-188.

[24] Freedman, S., and Jin, G. Z. Do social networks solve information problems for peer-to-peer lending? evidence from Tech. rep., com .NET Institute Working Paper, 2008.

[25] Ghodselahi, A. A hybrid support vector machine ensemble model for credit scoring. International Journal of Computer Applications 17 (2011).

[26] Goldberg, D. Genetic Algorithms in Search, Optimization, and Machine Learning. Artificial Intelligence. Addison-Wesley, 1989.

[27] Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. Gene selection for cancer classification using support vector machines. Machine learning 46, 1-3 (2002), 389-422.

[28] Hamel, L. H. Knowledge discovery with support vector machines, vol. 3. John Wiley & Sons, 2011.

[29] Hand, D. J. Evaluating diagnostic tests: the area under the roc curve and the balance of errors. Statistics in Medicine 29, 14 (2010), 1502-1510.

[30] Hand, D. J., and Anagnostopoulos, C. When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance? Pattern Recognition Letters 34, 5 (2013), 492-495.

[31] Hans, H. Statlog (german credit data) data set, 2013. Available at

[32] Hardy, D. Regulatory Capture in Banking. IMF working paper. INTERNATIONAL MONETARY FUND, 2006.

[33] Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Series in Statistics. Springer, 2009.

[34] Haykin, S. Neural Networks and Learning Machines. No. sv. 10 in Neural networks and learning machines. Prentice Hall, 2009.

[35] Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al. A practical guide to support vector classification, 2003.

[36] Huang, C.-L., Chen, M.-C., and Wang, C.-J. Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33, 4 (2007), 847-856.

[37] isePankur AS. data export. Available at

[38] Jebara, T. Machine Learning: Discriminative and Generative. The Springer International Series in Engineering and Computer Science. Springer US, 2004.

[39] Kim, E. Everything you wanted to know about the kernel trick (but were too afraid to ask)., September 2013.

[40] Kreyszig, E. Advanced Engineering Mathematics. John Wiley & Sons, 2010.

[41] Lending Club. Individual retirement accounts iras can provide tax advantaged growth, 2014. Available online at

[42] Lessmann, S., Seowb, H.-V., Baesens, B., and Thomas, L. C. Benchmarking state-of-the-art classification algorithms for credit scoring: A ten-year update. In Credit Research Centre, Conference Archive (2013).

[43] Li, J., Liu, J., Xu, W., and Shi, Y. Support vector machines approach to credit assessment. In Computational Science-ICCS 2004. Springer, 2004, pp. 892-899.

[44] Ng, A. Lecture notes. CS 229: Machine learning. Stanford University (2003).

[45] Ng, A. Machine Learning (Stanford)|Lecture 07., 2008.

[46] Ng, A. Machine Learning (Stanford)|Lecture 11., 2008.

[47] Ng, A. Y., and Jordan, M. I. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems 2 (2002), 841-848.

[48] Pender, W., Saddler, D., Shea, J., and Ward, D. Cambridge 2 Unit Mathematics Year 11 Enhanced Version PDF Textbook. Cambridge Secondary Maths (Australia) Series. Cambridge University Press, 2012.

[49] Peng, Y. Tikz example – svm trained with samples from two classes, September 2013.

[50] Platt, J. C. Fast training of support vector machines using sequential minimal optimization. In Advances in kernel methods (1999), MIT press, pp. 185-208.

[51] Rao, K., and Koolagudi, S. Emotion Recognition using Speech Features. SpringerBriefs in Electrical and Computer Engineering. Springer, 2012.

[52] Rummelhart, D. Learning representations by back-propagating errors. Nature 323, 9 (1986), 533-536.

[53] Sarle, W. S. Neural Network FAQ, part 2 of 7: Learning.

[54] Schebesch, K. B., and Stecking, R. Support vector machines for classifying and describing credit applicants: detecting typical and critical regions. Journal of the Operational Research Society 56, 9 (2005), 1082-1088.

[55] Schölkopf, B., and Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive computation and machine learning. MIT Press, 2002.

[56] Tvaroh, T. Using data mining methods in the analysis of credit risk data. Master’s thesis, University of Economics in Prague, 2014. [in Czech language].

[57] Williams, C. Support Vector Machines. School of Informatics, University of Edinburgh, October 2008.

[58] Witten, I., Frank, E., and Hall, M. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier Science, 2011.

[59] Witzany, J. Credit Risk Management and Modeling. Oeconomica, 2010.

[60] Xia, F. Support vector machine, 2008.

Leave a Reply

Your email address will not be published. Required fields are marked *