We define an algorithmic paradigm, the stack model, that captures many primal-dual and local-ratio algorithms for approximating covering and packing problems. The stack model is ...
The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with support vector machines. For a special c...
We describe an algorithm for learning in the presence of multiple criteria. Our technique generalizes previous approaches in that it can learn optimal policies for all linear pref...
Recently there has been considerable interest in learning with higher order relations (i.e., three-way or higher) in the unsupervised and semi-supervised settings. Hypergraphs and...
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and i...