Abstract

We introduce a class of flexible conditional probability models and techniques for classification /regression problems. Many existing methods such as generalized linear models and support vector machines are subsumed under this class. The flexibility of this class of techniques comes from the use of kernel functions as in support vector machines, and the generality from dual formulations of standard regression models. 1 Introduction Support vector machines [10] are linear maximum margin classifiers exploiting the idea of a kernel function. A kernel function defines an embedding of examples into (high or infinite dimensional) feature vectors and allows the classification to be carried out in the feature space without ever explicitly representing it. While support vector machines are non-probabilistic classifiers they can be extended and formalized for probabilistic settings[12] (recently also [8]), which is the topic of this paper. We can also identify the new formulations with other s...

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