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- R. Herbrich, T. Graepel,
and K. Obermayer. Support Vector Learning for Ordinal Regression.
.
In 9th International Conference on Artificial Neural Networks -
ICANN99, pages 97-102, 1999.
(FTP Gzipped PostScript, 6 pages, 40 kb)
We investigate the problem of predicting variables of ordinal
scale. This task is referred to as em 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 new learning algorithm for the task of ordinal regression based on
large margin rank boundaries. We give experimental results for an information
retrieval task: learning the order of documents w.r.t.an initial query.
Experimental results indicate that the presented algorithm outperforms more
naive approaches to ordinal regression such as Support Vector classification
and Support Vector regression in the case of more than two
ranks.
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