Assessing the quality of discovered results is an important open problem in data mining. Such assessment is particularly vital when mining itemsets, since commonly many of the disc...
In this paper, we address an issue that arises when the background knowledge used by explanationbased learning is incorrect. In particular, we consider the problems that can be ca...
We present a description of three different algorithms that use background knowledge to improve text classifiers. One uses the background knowledge as an index into the set of tra...
Subgroup discovery can be applied for exploration or descriptive induction in order to discover "interesting" subgroups of the general population, given a certain proper...
The use of background knowledge and the adoption of Horn clausal logic as a knowledge representation and reasoning framework are the distinguishing features of Inductive Logic Prog...
Abstract— In this paper we suggest a new approach to represent text document collections, integrating background knowledge to improve clustering effectiveness. Background knowled...
The benefit of incorporating background knowledge in the learning process has been successfully demonstrated in numerous applications of ILP methods. Nevertheless the effect of inc...
The importance of background knowledge for effective searching on the Web is not well understood. Participants were given trivia questions on two topics and asked to answer them f...
In the equivalent transformation (ET) computation model, a specification provides background knowledge in a problem domain, a program is a set of prioritized rewriting rules, and c...
Abstract. The use of a medical guideline can be seen as the execution of computational tasks, sequentially or in parallel, in the face of patient data. It has been shown that many ...
Arjen Hommersom, Perry Groot, Peter J. F. Lucas, M...