Learning from examples in FOL, also known as Inductive Logic Programming (ILP) (Muggleton & Raedt, 1994), constitutes a central topic in Machine Learn-ing, with relevant applications to problems in complex domains like natural lan-guage and molecular computational biology (Muggleton, 1999). Given a FO
. This paper provides a brief introduction and overview of the emerging area of Inductive Constrain...
In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the...
Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially use...
Contains fulltext : 84517.pdf (publisher's version ) (Closed access)Sixth Annual G...
This paper presents an overview of recent systems for Inductive Logic Programming (ILP). After a sho...
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic progra...
Inductive inference techniques are showing signs of maturity as applications with real-world data ar...
AbstractInductive Logic Programming (ILP) is the area of AI which deals with the induction of hypoth...
Concept learning is the induction of a description from a set of examples. Inductive logic programmi...
We summarise recent work on using Inductive Logic Programming (ILP) for Natural Language Processing ...
Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and ...
The current paper presents a brief overview of Inductive logic programming (ILP) systems. ILP algori...
Abstract The last three decades has seen the development of Computational Logic techniques within Ar...
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypo...
. This paper provides a brief introduction and overview of the emerging area of Inductive Constrain...
In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the...
Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially use...
Contains fulltext : 84517.pdf (publisher's version ) (Closed access)Sixth Annual G...
This paper presents an overview of recent systems for Inductive Logic Programming (ILP). After a sho...
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic progra...
Inductive inference techniques are showing signs of maturity as applications with real-world data ar...
AbstractInductive Logic Programming (ILP) is the area of AI which deals with the induction of hypoth...
Concept learning is the induction of a description from a set of examples. Inductive logic programmi...
We summarise recent work on using Inductive Logic Programming (ILP) for Natural Language Processing ...
Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and ...
The current paper presents a brief overview of Inductive logic programming (ILP) systems. ILP algori...
Abstract The last three decades has seen the development of Computational Logic techniques within Ar...
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypo...
. This paper provides a brief introduction and overview of the emerging area of Inductive Constrain...
In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the...
Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially use...
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