Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current cluster...

This paper provides a systematic study of incremental learning from noise-free and from noisy data. As usual, we distinguish between learning from positive data and learning from positive and negative data, synonymously called learning from text and learning from informant. Our study relies on the notion of noisy data introduced by Stephan. The basic scenario, named iterative learning, is as fo...

The paper provides a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every in nite sequence of elements exhausting c is called positive presentation of c. An algorithmic learner takes as input one element of a positive presentation and its previously made hypothesis at a time, and outputs a new hypothesis about the target conce...