Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a net...
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intr...
Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given tas...
Jonathan Clark, Robert E. Frederking, Lori S. Levi...