Add to ORKGAbstract We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we show how to upgrade another algorithm for learning Bayesian networks, namely ordering-search. For Bayesian networks, ordering-search was found to work better than structure-search. It is non-obvious that these results carry over to the relational case, however, since there ordering-search needs to be implemented quite differently. Hence, we perform an experimental comparison of these upgraded algorithms on four relational domains. We conclude that also in the relational case ordering-search is comp...
International audienceProbabilistic relational models (PRMs) extend Bayesian networks (BNs) to a rel...
In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for eac...
Automatisch leren (``machine learning'') is de studie van algoritmenvoor het leren van modellen uit ...
We discuss how to learn non-recursive directed probabilistic logical models from relational data. Th...
Abstract. We discuss how to learn non-recursive directed probabilistic logical models from relationa...
There is an increasing interest in upgrading Bayesian networks to the relational case, resulting in ...
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models a...
Recently, there has been an increasing interest in probabilistic logical models and a variety of suc...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distr...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Tractable Bayesian network learning’s goal is to learn Bayesian networks (BNs) where inference is gu...
International audienceProbabilistic relational models (PRMs) extend Bayesian networks (BNs) to a rel...
In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for eac...
Automatisch leren (``machine learning'') is de studie van algoritmenvoor het leren van modellen uit ...
We discuss how to learn non-recursive directed probabilistic logical models from relational data. Th...
Abstract. We discuss how to learn non-recursive directed probabilistic logical models from relationa...
There is an increasing interest in upgrading Bayesian networks to the relational case, resulting in ...
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models a...
Recently, there has been an increasing interest in probabilistic logical models and a variety of suc...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distr...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Tractable Bayesian network learning’s goal is to learn Bayesian networks (BNs) where inference is gu...
International audienceProbabilistic relational models (PRMs) extend Bayesian networks (BNs) to a rel...
In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for eac...
Automatisch leren (``machine learning'') is de studie van algoritmenvoor het leren van modellen uit ...