kernel logistic regression and the import vector machine

[Ji Zhu]

The following short paper is available at
http://www.stanford.edu/~jzhu/research/nips01.ps


	Kernel Logistic Regression and the Import Vector Machine
		Ji Zhu, Trevor Hastie
	Dept. of Statistics, Stanford University

		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.