We propose a new local learning scheme that is based on the principle of decisiveness: the learned classifier is expected to exhibit large variability in the direction of the test ...
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Representing documents by vectors that are independent of language enhances machine translation and multilingual text categorization. We use discriminative training to create a pr...
Delivering linguistic resources and easy-to-use methods to a broad public in the humanities is a challenging task. On the one hand users rightly demand easy to use interfaces but ...
In this paper we study the problem of collecting training samples for building enterprise taxonomies. We develop a computer-aided tool named InfoAnalyzer, which can effectively as...