Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric
used to compute distances between different examples. In this paper, we show how to learn …

We study trading systems using reinforcement learning with three newly proposed methods
to maximize total profits and reflect real financial market situations while overcoming the …

We present a family of margin based online learning algorithms for various prediction tasks.
In particular we derive and analyze algorithms for binary and multiclass categorization …

In this paper, we present an information-theoretic approach to learning a Mahalanobis
distance function. We formulate the problem as that of minimizing the differential relative …

We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN)
classification by semidefinite programming. The metric is trained with the goal that the k …