Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Ji Zhu, Trevor Hastie
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 per- forms 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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