Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear supp...
Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can...
The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm for the infinite Hidden Markov...
Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubi...
This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training...
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. ...
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal...