Support Vector Learning for Ordinal Regression

@inproceedings{Herbrich1999SupportVL,
  title={Support Vector Learning for Ordinal Regression},
  author={Ralf Herbrich and Thore Graepel and Klaus Obermayer},
  year={1999}
}
We investigate the problem of predicting variables of ordinal scale. This taks is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. The approach presented is based on a mapping from objects to scalar utility values. Similar to Support Vector methods we derive a… CONTINUE READING
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