Support vector machines for classifying and describing credit applicants: detecting typical and critical regions

@article{Schebesch2005SupportVM,
  title={Support vector machines for classifying and describing credit applicants: detecting typical and critical regions},
  author={Klaus B. Schebesch and Ralf Stecking},
  journal={JORS},
  year={2005},
  volume={56},
  pages={1082-1088}
}
Credit applicants are assigned to good or bad risk classes according to their record of defaulting. Each applicant is described by a high-dimensional input vector of situational characteristics and by an associated class label. A statistical model, which maps the inputs to the labels, can decide whether a new credit applicant should be accepted or rejected, by predicting the class label given the new inputs. Support vector machines (SVM) from statistical learning theory can build such models… CONTINUE READING
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