We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the ...
It is investigated for which choice of a parameter q, denoting the number of contexts, the class of simple external contextual languages is iteratively learnable. On one hand, the ...
Leonor Becerra-Bonache, John Case, Sanjay Jain, Fr...
The question investigated in this paper is to what extent an input representation influences the success of learning, in particular from the point of view of analyzing agents that...
Abstract. The nearest neighbor and the perceptron algorithms are intuitively motivated by the aims to exploit the “cluster” and “linear separation” structure of the data to...
In this paper we discuss boosting algorithms that maximize the soft margin of the produced linear combination of base hypotheses. LPBoost is the most straightforward boosting algor...
Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vish...
In this paper, we initiate a theoretical study of the problem of clustering data under interactive feedback. We introduce a query-based model in which users can provide feedback to...
Abstract. Classical probability theory considers probability distributions that assign probabilities to all events (at least in the finite case). However, there are natural situat...
Alexey V. Chernov, Alexander Shen, Nikolai K. Vere...