Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional ...
Abstract: This chapter contains sections titled: Introduction, Logic Programming, Inductive Logic Programming: Settings and Approaches, Relational Classification Rules, Relational Decision Trees, ...
Probabilistic Inductive Logic Programming [paper] Brief overview: The paper outlines three classical settings for inductive logic programming, namely learning from entailment, learning from ...
Complete implementation of Inductive Logic Programming algorithms with full research accuracy. Includes FOIL (Quinlan 1990) and Progol (Muggleton 1995) with comprehensive configuration. Muggleton, S. ...
Abstract: The Web has become an extremely large source of information and also a platform of various e-service including e-business, e-science, e-learning, e-government, etc. How to develop the new ...
Inductive Logic Programming (ILP) is a well-established area of Machine Learning that studies the task of learning from observations in the context of logic programming. In this talk I will present ...
In this paper, we explored a learning approach which combines different learning methods in inductive logic programming (ILP) to allow a learner to produce more expressive hypothese than that of each ...
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