Knowledge refinement tools rely on a representative set of training examples to identify and repair faults in a knowledge based system (KBS). In real environments it is often diffi...
Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training examples. In the case of planning, examp...
Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. Each neurule is represented as an adaline unit. O...
Jim Prentzas, Ioannis Hatzilygeroudis, Athanasios ...
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
A major difficulty of supervised approaches for text classification is that they require a great number of training instances in order to construct an accurate classifier. This pap...
Recovering design patterns applied in a system can help refactoring the system. Machine learning algorithms have been successfully applied in mining data patterns. However, one of...
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Abstract. As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks,...
We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based ...
Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires mode...