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Prof. Dr. Jürgen Bitzer

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Annegret Kock

+49 (0)441 798-4117

+49 (0)441 798-4116

 A05 0-013

Catharina Schramm

+49 (0)441 798-4107

+49 (0)441 798-194107

A05 0-014
 

Carl von Ossietzky Universität Oldenburg
Fakultät II - Wirtschafts- und Rechtswissenschaften -
Institut für Volkswirtschaftslehre
Ammerländer Heerstr. 114-118
26129 Oldenburg

Veröffentlichungen Fachgebiet Statistik

Improving Classifier Performance by Using Fictitious Training Data? A Case Study
R. Stecking, K.B. Schebesch (2007)
accepted for publication in Operations Research Proceedings 2007

Using Multiple SVM Models for Unbalanced Credit Scoring Data Sets
K.B. Schebesch, R. Stecking (2007)
accepted for publication in Proceedings of the 31st International GfKl Conference, Freiburg

Combining Support Vector Machines for Credit Scoring
R. Stecking, K.B. Schebesch (2007)
Waldmann, K.-H., Stocker, U.M. (Eds.): Operations Research Proceedings 2006. Springer, Berlin 135-140

Selecting SVM Kernels and Input Variable Subsets in Credit Scoring Models
K.B. Schebesch, R. Stecking (2007)
Decker, R., Lenz, H.-J. (Eds.): Advances in Data Analysis. Springer, Berlin, 179-186

Variable Subset Selection for Credit Scoring with Support Vector Machines
R. Stecking, K.B. Schebesch (2006)
Haasis, H.-D., Kopfer, H. and Schönberger, J. (Eds.): Operations Research Proceedings 2005. Springer, Berlin 251-256

Comparing and Selecting SVM-Kernels for Credit Scoring
R. Stecking, K.B. Schebesch (2006)
Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A. and Gaul, W. (Eds.): From Data and Information Analysis to Knowledge Engineering. Springer, Berlin, 542-549

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

Extracting Rules from Support Vector Machines
K.B. Schebesch, R. Stecking (2005)
Fleuren, H., den Hertog, D. and Kort, P. (Eds.): Operations Research Proceedings 2004. Springer, Berlin 408-415

Using Support Vector Machines in Credit Scoring to select informative patterns and to extract rules from credit data pools
R. Stecking, K.B. Schebesch (2005)
Information & Knowledge Age. The Proceedings of the Seventh International Conference on Informatics in Economy. Inforec, Bucharest, 532-537

Support Vector Machines: Advanced Method for Credit Scoring
R. Stecking, K.B. Schebesch (2005)
Ehrig, D. and Staroske, U. (Eds.): Dimensionen angewandter Wirtschaftsforschung: Methoden, Regionen, Sektoren. Hamburg, 59-85

Informative Patterns for Credit Scoring Using Linear SVM
R. Stecking, K.B. Schebesch (2005)
Weihs, C. and Gaul, W. (Eds.): Classification – The Ubiquitous Challenge. Springer, Berlin, 450-457

Support Vector Machines for Credit Scoring: Extension to Non Standard Cases
K.B. Schebesch, R. Stecking (2005)
Baier, D. and Wernecke, K.-D. (Eds.): Innovations in Classification, Data Science and Information Systems. Springer, Berlin, 498-505

Data-oriented Artificial Intelligence I+II
K.B. Schebesch, R. Stecking (2003)
Master of International Business Informatics Handbook. Editura ASE, Bucharest, 257-264

Support Vector Machines with Applications to Credit Scoring
R. Stecking, K.B. Schebesch (2003)
Digital Economy. The Proceedings of the Sixth International Conference on Informatics in Economy. Inforec, Bucharest, 849-855

Credit Scoring im Baukreditwesen
R. Stecking (2003)
Schaefer, H. (Ed.): Kredit und Risiko: Basel II und die Konsequenzen für Banken und Mittelstand. Metropolis-Verlag, Marburg, 45-56

Support Vector Machines for Credit Scoring: Comparing to and Combining with some Traditional Classification Methods
R. Stecking, K.B. Schebesch (2003)
Schader, M., Gaul, W. and Vichi, M. (Eds.): Between Data Science and Applied Data Analysis. Springer, Berlin, 604-612

Marktsegmentierung mit Neuronalen Netzen
R. Stecking (2000)
DUV, Wiesbaden

We9gbmasterb/ (cathqnarijsisona.so0chra07mmfo@uol.drswe) (Stand: 19.11.2019)