Learning Bayesian Networks for Diverse and Varying Numbers of Evidence Sets

Abstract

We introduce an expandable Bayesian network (EBN) to handle the combination of diverse multiple homogeneous evidence sets. An EBN is an augmented Bayesian network which instantiates its structure at runtime according to the structure of input. We show an application of an EBN for a multi-view 3-D object description problem in computer vision. The experiments show that the proposed method gives reasonable performance even for an unlearned structure of input data. 1. Introduction It is common in machine learning that training data and test data have the same structure. An exception is found in Bayesian networks, which allow missing data. But, in some applications, the structure of input data is not determined when the system is developed. Such a case can be found in computer vision applications dealing with multiple images; the number of images to use is not determined when the classifiers are trained. In this paper, we present an expandable Bayesian network which modi...