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Abstract:
The support vector machine (SVM) is known for its good
performance in binary classification, but its extension to
multi-class classification is still an on-going research issue.
In this paper, we propose a new approach for classification,
called the import vector machine (IVM), which is built on kernel
logistic regression (KLR). We show that the IVM not only performs
as well as the SVM in binary classification, but also can
naturally be generalized to the multi-class case. Furthermore,
the IVM provides an estimate of the underlying probability.
Similar to the ``support points'' of the SVM, the IVM model uses
only a fraction of the training data to index kernel basis
functions, typically a much smaller fraction than the SVM. This
gives the IVM a computational advantage over the SVM, especially
when the size of the training data set is large.
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