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 need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

The metric learning problem is concerned with learning a distance function tuned to a
particular task, and has been shown to be useful when used in conjunction with nearest …

Learning an effective distance metric between image pairs plays an important role in visual
analysis, where the training procedure largely relies on hard negative samples. However …

Motivated by large-scale multimedia applications we propose to learn mappings from high-
dimensional data to binary codes that preserve semantic similarity. Binary codes are well …

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 …

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 …

This paper addresses the task of zero-shot image classification. The key contribution of the
proposed approach is to control the semantic embedding of images–one of the main …

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 …