The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming. Since the ...
Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the ...
A largely incomplete but hopefully useful list of links to datasets for relational learning and inductive logic programming. No guarantees on availability. Symbolic function approximator aims to ...
Abstract: Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically ...
Complete implementation of Inductive Logic Programming algorithms with full research accuracy. Includes FOIL (Quinlan 1990) and Progol (Muggleton 1995) with comprehensive configuration. Muggleton, S. ...
A developer’s work can get quite repetitive. This tedious part of his or her job decreases work time efficiency by a considerable amount. Inductive programming systems can provide a solution to this ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
99% of computer end users do not know programming and struggle with repetitive tasks. Inductive synthesis can revolutionize this landscape by enabling end users to automate repetitive tasks using ...
Empirical methods for building natural language systems has become an important area of research in recent years. Most current approaches are based on propositional learning algorithms and have been ...
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