We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments...
Workplace learning offers the unique possibility of the immediacy of purpose and real-world context. In order to leverage on this, we have developed a context-aware method to suppo...
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propo...
We consider the bicriteria scheduling problem of minimizing the number of tardy jobs and average flowtime on a single machine. This problem, which is known to be NP-hard, is impor...
: Sequential algorithms given by Angluin 1987 and Schapire 1992 learn deterministic nite automata DFA exactly from Membership and Equivalence queries. These algorithms are feasible...