In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of
hypotheses generated by an arbitrary on-line learning algorithm run on an independent and …

Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like
algorithms, are among the best available techniques for solving pattern classification …

In this paper we show that on-line algorithms for classification and regression can be naturally
used to obtain hypotheses with good datadependent tail bounds on their risk. Our results …

Serious games are computer games used as educational technology or as a vehicle for
presenting or promoting a point of view. Serious games are intended to provide an engaging, self…

In this paper we investigate selective sampling, a learning model where the learner observes
a sequence of iid unlabeled instances each time deciding whether to query the label of the …

We introduce a variant of the Perceptron algorithm called second-order Perceptron algorithm,
which is able to exploit certain spectral properties of the data. We analyze the second-…

We study the problem of classifying data in a given taxonomy when classifications associated
with multiple and/or partial paths are allowed. We introduce an incremental algorithm …

This chapter presents a distributed and open ICT infrastructure called ICT-I that has been
implemented in the ECOLEAD project to help members of Collaborative Networks in doing …

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Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web
services on the basis of comprehensive, machine-interpretable semantic descriptions. Since …