Special Colloquia, Department of Statistics Wednesday, January 29, 2003 4:30 PM in REC 307 Mr. Ji Zhu Stanford University will speak on Support Vector Machine, Kernel Logistic Regression and Import Vector Machine 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 talk, we propose a new approach for classification, called the import vector machine (IVM), whichis 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 potentially computational advantage over the SVM, especially when the size of the training data set is large.

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