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Some applications of Inductive Logic Programming (ILP) are presented. Those applications are chosen that specifically benefit from <i>relational</i> descriptions generated by ILP programs, and from ILP's ability to accommodate background knowledge. Applications included are: drug design, predicting the secondary structure of proteins, and design of(More)
Many effective and efficient learning algorithms assume independence of attributes. They often perform well even in domains where this assumption is not really true. However, they may fail badly when the degree of attribute dependencies becomes critical. In this paper, we examine methods for detecting deviations from independence. These dependencies give(More)
There is much empirical evidence about the success of naive Bayesian classification (NBC) in medical applications of attribute-based machine learning. NBC assumes conditional independence between attributes. In classification, such classifiers sum up the pieces of class-related evidence from individual attributes, independently of other attributes. The(More)