Some applications of collective learning

Abstract

Much of the real-world data have complex dependencies between the individual tuples. For example, the chance that a patient has a particular disease depends on the prevalence of the disease in the immediate neighborhood. One approach to handling such linked data is “collective learning.” In collective learning, one deals with a set of data points taken at a time. The dependencies between the data points are modeled as a graph, with the nodes representing the tuples and the edges between them representing the influence of the tuples on one another. A variety of domains lend themselves naturally to such graph-based modeling. There have been a variety of collective learning and inferencing approaches that have been proposed in the literature. In this talk, I will give a brief introduction to collective learning and describe two applications.