Abstract. When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional ...
We study the problem of learning a classification task in which only a dissimilarity function of the objects is accessible. That is, data are not represented by feature vectors bu...
In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is kno...
NLP tasks are often domain specific, yet systems can learn behaviors across multiple domains. We develop a new multi-domain online learning framework based on parameter combinatio...
We report on three distinct experiments that provide new valuable insights into learning algorithms and datasets. We first describe two effective meta-features that significantly ...