Probabilistic kernel regression models
We introduce a class of exible conditional probability models and techniques for classi cation regression problems Many existing methods such as generalized linear models and support vector machines are subsumed under this class The exibility of this class of techniques comes from the use of kernel functions as in support vector machines and the generality from dual formulations of stan dard regression models