We study the problem of learning parity functions that depend on at most k variables (kparities) attribute-efficiently in the mistake-bound model. We design a simple, deterministi...
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operat...
Seeking to extend the functional capability of the elderly, we explore the use of probabilistic methods to learn and recognise human activity in order to provide monitoring suppor...
We present feature transformations useful for exploratory data analysis or for pattern recognition. Transformations are learned from example data sets by maximizing the mutual inf...
This paper describes the winning entry to the Omphalos context free grammar learning competition. Our approach integrates an information theoretic constituent likelihood measure to...