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Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 811 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects
Hybrid Markov Logic Networks
"... Markov logic networks (MLNs) combine firstorder logic and Markov networks, allowing us to handle the complexity and uncertainty of realworld problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most realworld applications also contain continuo ..."
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Cited by 42 (1 self)
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Markov logic networks (MLNs) combine firstorder logic and Markov networks, allowing us to handle the complexity and uncertainty of realworld problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most realworld applications also contain
Learning the structure of Markov logic networks
 In Proceedings of the 22nd International Conference on Machine Learning
, 2005
"... Markov logic networks (MLNs) combine logic and probability by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. In this paper we develop an algorithm for learning the structure of MLNs from relational databases, combining ideas from inductive l ..."
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Cited by 114 (20 self)
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Markov logic networks (MLNs) combine logic and probability by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. In this paper we develop an algorithm for learning the structure of MLNs from relational databases, combining ideas from inductive
Discriminative training of markov logic networks
 In Proc. of the Natl. Conf. on Artificial Intelligence
, 2005
"... Many machine learning applications require a combination of probability and firstorder logic. Markov logic networks (MLNs) accomplish this by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can be lear ..."
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Cited by 108 (19 self)
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Many machine learning applications require a combination of probability and firstorder logic. Markov logic networks (MLNs) accomplish this by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can
Reinforcement learning with markov logic networks
 In Proceedigns of European Workshop on Reinforcement Learning
, 2008
"... Abstract. In this paper, we propose a method to combine reinforcement learning (RL) and Markov logic networks (MLN). RL usually does not consider the inherent relations or logical connections of the features. Markov logic networks combines firstorder logic and graphical model and it can represent a ..."
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Cited by 2 (0 self)
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Abstract. In this paper, we propose a method to combine reinforcement learning (RL) and Markov logic networks (MLN). RL usually does not consider the inherent relations or logical connections of the features. Markov logic networks combines firstorder logic and graphical model and it can represent
Transfer learning with Markov logic networks
 2006. Proceedings of the ICML06 Workshop on Structural Knowledge Transfer for Machine Learning
"... We propose a new algorithm for transfer learning of Markov Logic Network (MLN) structure. An important aspect of our approach is that it first diagnoses the provided source MLN and then focuses on relearning only the incorrect portions. Experiments in a pair of synthetic domains demonstrate that t ..."
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Cited by 8 (2 self)
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We propose a new algorithm for transfer learning of Markov Logic Network (MLN) structure. An important aspect of our approach is that it first diagnoses the provided source MLN and then focuses on relearning only the incorrect portions. Experiments in a pair of synthetic domains demonstrate
On Generative Parameterisations of Markov logic networks
"... Given some fixed firstorder language, a Markov logic network (MLN) (Domingos et al., 2008) uses weighted formulae to define a probability distribution over Herbrand interpretations of the language. (To save space a Herbrand interpretation will be called a ‘world’ from now on. In this paper all such ..."
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Given some fixed firstorder language, a Markov logic network (MLN) (Domingos et al., 2008) uses weighted formulae to define a probability distribution over Herbrand interpretations of the language. (To save space a Herbrand interpretation will be called a ‘world’ from now on. In this paper all
Discriminative Training of Markov Logic Networks
"... Many machine learning applications require a combination of probability and firstorder logic. Markov logic networks (MLNs) accomplish this by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can be lear ..."
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Many machine learning applications require a combination of probability and firstorder logic. Markov logic networks (MLNs) accomplish this by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can
Discriminative Learning with Markov Logic Networks
"... Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from noisy structured/relational data. Markov logic networks (MLNs), sets of weighted clauses, are a simple but powerful SRL formalism that combines the expressivity of firstorder logic with ..."
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Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from noisy structured/relational data. Markov logic networks (MLNs), sets of weighted clauses, are a simple but powerful SRL formalism that combines the expressivity of firstorder logic
Event modeling and recognition using markov logic networks
 IN ECCV
, 2008
"... We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as firstorder logic production rules with associated weights to indicate their con ..."
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Cited by 83 (4 self)
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their confidence. These rules are used in combination with a relaxed deduction algorithm to construct a network of grounded atoms, the Markov Logic Network. The network is used to perform probabilistic inference for input queries about events of interest. The system’s performance is demonstrated on a number
Results 1  10
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