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Handling Continuous-Valued Attributes in Incremental First-Order Rules Learning
[chapter]
2005
Lecture Notes in Computer Science
Machine Learning systems are often distinguished according to the kind of representation they use, which can be either propositional or first-order logic. The framework working with first-order logic as a representation language for both the learned theories and the observations is known as Inductive Logic Programming (ILP). It has been widely shown in the literature that ILP systems have limitations in dealing with large amounts of numerical information, that is however a peculiarity of most
doi:10.1007/11558590_43
fatcat:jcxdhkfe75chlbnr3uge45ikmi