Instance-Based Learning Algorithms
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Analogical Logic Program Synthesis from Examples
ECML '95 Proceedings of the 8th European Conference on Machine Learning
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Distance Between Herbrand Interpretations: A Measure for Approximations to a Target Concept
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning Functions from Imperfect Positive Data
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
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The principles of instance based function learning are presented. In IBFL one is given a set of positive examples of a functional predicate. These examples are true ground facts that illustrate the input output behaviour of the predicate. The purpose is then to predict the output of the predicate given a new input. Further assumptions are that there is no background theory and that the inputs and outputs of the predicate consist of structured terms. IBFL is a novel technique that addresses this problem and that combines ideas from instance based learning, first order distances and analogical or case based reasoning. We also argue that IBFL is especially useful when there is a need for handling complex and deeply nested terms. Though we present the technique in isolation, it might be more useful as a component of a larger system to deal e.g. with the logic, language and learning challenge.