Learning multiple classifiers with Dirichlet process mixture priors
Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram · 2005
Introduction A real world classification task can often be viewed as consisting of multi-ple subtasks. In remote sensing, for example, one may have multiple sets of radar images, each collected at a particular geographical location, with the aim of designing classifiers for detecting objects of interest in images at all locations. In this situation, one can either learn a single classifier from simple pooling of images from different locations; or learn multi-