Add to ORKGWe propose a new algorithm for transfer learning of Markov Logic Network (MLN) structure. An important aspect of our ap-proach is that it first diagnoses the provided source MLN and then focuses on re-learning only the incorrect portions. Experiments in a pair of synthetic domains demonstrate that this strategy significantly decreases the search space and speeds up learning while maintaining a level of accuracy comparable to that of the current best algorithm. 1
Abstract. Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powe...
Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that comb...
The structure of a Markov network is typically learned in one of two ways. The first approach is to ...
Markov logic networks (MLNs) generalize first-order logic and probabilistic graphical models, using ...
This article argues that currently the largest gap between human and machine learning is learning al...
This article argues that currently the largest gap between human and machine learning is learning al...
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Ma...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
textTraditionally, machine learning algorithms assume that training data is provided as a set of ind...
Markov logic networks (MLNs) are a popular statistical relational learning formalism that com-bine M...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Many real-world applications of AI require both probability and first-order logic to deal with uncer...
Standard inductive learning requires that training and test instances come from the same distributio...
textMany real-world problems involve data that both have complex structures and uncertainty. Statist...
Abstract. Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which ...
Abstract. Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powe...
Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that comb...
The structure of a Markov network is typically learned in one of two ways. The first approach is to ...
Markov logic networks (MLNs) generalize first-order logic and probabilistic graphical models, using ...
This article argues that currently the largest gap between human and machine learning is learning al...
This article argues that currently the largest gap between human and machine learning is learning al...
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Ma...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
textTraditionally, machine learning algorithms assume that training data is provided as a set of ind...
Markov logic networks (MLNs) are a popular statistical relational learning formalism that com-bine M...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Many real-world applications of AI require both probability and first-order logic to deal with uncer...
Standard inductive learning requires that training and test instances come from the same distributio...
textMany real-world problems involve data that both have complex structures and uncertainty. Statist...
Abstract. Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which ...
Abstract. Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powe...
Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that comb...
The structure of a Markov network is typically learned in one of two ways. The first approach is to ...