Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 14, Issue 1, Pages 185-205Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/106186005X25619
Keywords
classification; Kernel methods; multiclass learning; radial basis; reproducing kernel Hilbert space (RKHS); support vector machines
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The support vector machine (SVM) is known for its good performance in two-class classification, but its extension to multiclass classification is still an ongoing research issue. In this article, 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 two-class classification, but also can naturally be generalized to the multiclass 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 potential computational advantage over the SVM.
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