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Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder r ..."
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Cited by 1181 (79 self)
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Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first
Parameter Estimation in Stochastic Logic Programs
 Machine Learning
, 2000
"... . Stochastic logic programs (SLPs) are logic programs with labelled clauses which dene a loglinear distribution over refutations of goals. The loglinear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions ..."
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Cited by 82 (5 self)
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. Stochastic logic programs (SLPs) are logic programs with labelled clauses which dene a loglinear distribution over refutations of goals. The loglinear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex
Learning Stochastic Logic Programs
 WORKINPROGRESS TRACK AT THE 11TH INTERNATIONAL CONFERENCE ON INDUCTIVE LOGIC PROGRAMMING
, 2001
"... Most existing structured data is stored in relational form. Inductive ..."
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Cited by 3 (2 self)
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Most existing structured data is stored in relational form. Inductive
Semantics and derivation for Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) are a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and undirected Bayes' nets. A pure stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0; 1] and C is a firstorder rangerest ..."
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Cited by 7 (0 self)
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Stochastic Logic Programs (SLPs) are a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and undirected Bayes' nets. A pure stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0; 1] and C is a firstorder range
Semantics and derivation for Stochastic Logic Programs
"... Stochastic Logic Programs (SLPs) are a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and undirected Bayes ’ nets. A pure stochastic logic program consists of a set of labelled clauses : ¡ where is in the interval ¢£¥¤§¦© ¨ and C is a firstorder rangerestricted d ..."
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Stochastic Logic Programs (SLPs) are a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and undirected Bayes ’ nets. A pure stochastic logic program consists of a set of labelled clauses : ¡ where is in the interval ¢£¥¤§¦© ¨ and C is a firstorder range
Stochastic Logic Programs: Sampling, Inference and Applications
 In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI2000
, 2000
"... Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show how SLPs can be used to represent prior distributions for machine learning, using (i) logic programs and (ii) Baye ..."
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Cited by 29 (5 self)
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Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show how SLPs can be used to represent prior distributions for machine learning, using (i) logic programs and (ii
Computing Confidence Measures in Stochastic Logic Programs
"... Abstract. Stochastic logic programs (SLPs) provide an efficient representation for complex tasks such as modelling metabolic pathways. In recent years, methods have been developed to perform parameter and structure learning in SLPs. These techniques have been applied for estimating rates of enzymec ..."
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Abstract. Stochastic logic programs (SLPs) provide an efficient representation for complex tasks such as modelling metabolic pathways. In recent years, methods have been developed to perform parameter and structure learning in SLPs. These techniques have been applied for estimating rates of enzyme
MultiClass Prediction Using Stochastic Logic Programs
"... Abstract. In this paper, we present a probabilistic method of dealing with multiclass classification using Stochastic Logic Programs (SLPs), a Probabilistic Inductive Logic Programming (PILP) framework that integrates probability, logic representation and learning. Multiclass prediction attempts to ..."
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Cited by 2 (0 self)
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Abstract. In this paper, we present a probabilistic method of dealing with multiclass classification using Stochastic Logic Programs (SLPs), a Probabilistic Inductive Logic Programming (PILP) framework that integrates probability, logic representation and learning. Multiclass prediction attempts
Towards learning stochastic logic programs from proofbanks
 In Proc. of AAAI’05
, 2005
"... Stochastic logic programs combine ideas from probabilistic grammars with the expressive power of definite clause logic; as such they can be considered as an extension of probabilistic contextfree grammars. Motivated by an analogy with learning treebank grammars, we study how to learn stochastic lo ..."
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Cited by 8 (3 self)
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Stochastic logic programs combine ideas from probabilistic grammars with the expressive power of definite clause logic; as such they can be considered as an extension of probabilistic contextfree grammars. Motivated by an analogy with learning treebank grammars, we study how to learn stochastic
A comparison of stochastic logic programs and Bayesian logic programs
 In IJCAI03 Workshop on Learning Statistical Models from Relational Data. IJCAI
, 2003
"... Firstorder probabilistic models are recognized as efficient frameworks to represent several realworld problems: they combine the expressive power of firstorder logic, which serves as a knowledge representation language, and the capability to model uncertainty with probabilities. Among existing mod ..."
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Cited by 19 (4 self)
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models, it is usual to distinguish the domainfrequency approach from the possibleworlds approach. Bayesian logic programs (BLPs, which conveniently encode possibleworlds semantics) and stochastic logic programs (SLPs, often referred to as a domainfrequency approach) are promising probabilistic logic
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