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Nonmonotonic Inductive Logic Programming
 In Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
"... Nonmonotonic logic programming (NMLP) and inductive logic programming (ILP) are two important extensions of logic programming. ..."
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Cited by 10 (1 self)
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Nonmonotonic logic programming (NMLP) and inductive logic programming (ILP) are two important extensions of logic programming.
Probabilistic inductive logic programming
 In ALT
, 2004
"... Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of diffe ..."
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Cited by 70 (9 self)
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Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety
Parallel Inductive Logic Programming
 In Proceedings of the MLnet Familiarization Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases
, 1995
"... The generic task of Inductive Logic Programming (ILP) is to search a predefined subspace of firstorder logic for hypotheses that in some respect explain examples and background knowledge. In this paper we consider the development of parallel implementations of ILP systems. A first part discusses th ..."
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Cited by 14 (1 self)
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The generic task of Inductive Logic Programming (ILP) is to search a predefined subspace of firstorder logic for hypotheses that in some respect explain examples and background knowledge. In this paper we consider the development of parallel implementations of ILP systems. A first part discusses
Inductive Logic Programming for Data Mining
, 2004
"... This paper addresses the problem of data mining in Inductive Logic Programming (ILP) motivated by its application in the domain of economics. ..."
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This paper addresses the problem of data mining in Inductive Logic Programming (ILP) motivated by its application in the domain of economics.
Constraint Inductive Logic Programming
, 1996
"... . This paper is concerned with learning from positive and negative examples expressed in firstorder logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discrim ..."
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Cited by 22 (6 self)
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. This paper is concerned with learning from positive and negative examples expressed in firstorder logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A
Contributions to Inductive Logic Programming
, 1996
"... Contents Preface iii 1 What is Inductive Logic Programming? 1 1.1 The importance of learning : : : : : : : : : : : : : : : : : : : : : 1 1.2 Inductive learning : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.3 The problem setting for ILP : : : : : : : : : : : : : : : : : : : : : 4 1.4 Other ..."
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Contents Preface iii 1 What is Inductive Logic Programming? 1 1.1 The importance of learning : : : : : : : : : : : : : : : : : : : : : 1 1.2 Inductive learning : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.3 The problem setting for ILP : : : : : : : : : : : : : : : : : : : : : 4 1
Stochastic Inductive Logic Programming
, 1994
"... Machine learning is an important part of artificial intelligence and its applications. Learning from instances is one of the most active areas within machine learning. Initial successes in the induction of propositional theories have been followed by algorithms that construct hypotheses in the form ..."
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of (a subset of) the first order relational concepts. Such learning is called Inductive Logic Programming (ILP). This thesis deals with two key problems of machine learning of concepts from instances: hypothesis justification and hypothesis construction which are also a vital part of the form
Anytime Inductive Logic Programming
 In Proceedings of the 15th International Conference on Computers and Their Applications
, 2000
"... Anytime algorithms refers to algorithms that \always " can produce a result. Often the result of the algorithm depends on the time at hand, the longer the time, the better the answer. In this paper we present an easy way of turning regular Inductive Logic Programming (ILP) algorithms such as Di ..."
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Cited by 2 (0 self)
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Anytime algorithms refers to algorithms that \always " can produce a result. Often the result of the algorithm depends on the time at hand, the longer the time, the better the answer. In this paper we present an easy way of turning regular Inductive Logic Programming (ILP) algorithms
Classifier (MP2D), Inductive Logic Programming
"... Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds ..."
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Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds
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