The pairwise approach to multilabel classification reduces the problem to learning and aggregating preference predictions among the possible labels. A key problem is the need to query a quadratic… (More)

This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a preference-based… (More)

We extend the multi-label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the… (More)

Pairwise classification is a class binarization procedure that converts a multi-class problem into a series of two-class problems, one problem for each pair of classes. While it can be shown that for… (More)

This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a “preference-based”… (More)

The poker agent AKI-REALBOT described in this paper was designed to participate in the 6-player Limit competition which was part of the Computer Poker Challenge at the AAAI 2008 conference.It ended… (More)

This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a preference-based… (More)

Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary… (More)

Pairwise classification is a class binarization procedure that converts a multi-class problem into a series of two-class problems, one problem for each pair of classes. While it can be shown that for… (More)