Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area h...
The wealth of information contained in the world-wide web has created much interest in systems for integrating information from multiple sites. We describe a universal wrapper mac...
Graphplan and heuristic state space planners such as HSP-R and UNPOP are currently two of the most effective approaches for solving classical planning problems. These approaches h...
One of the surprising findings from the study of CNF satisfiability in the 1990's has been the success of iterative repair techniques, and in particular of weighted iterative...
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...