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» Learning the structure of Markov logic networks
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ICANN
2010
Springer
13 years 8 months ago
Neuro-symbolic Representation of Logic Programs Defining Infinite Sets
It has been one of the great challenges of neuro-symbolic integration to represent recursive logic programs using neural networks of finite size. In this paper, we propose to imple...
Ekaterina Komendantskaya, Krysia Broda, Artur S. d...
AAAI
2008
13 years 10 months ago
Markov Blanket Feature Selection for Support Vector Machines
Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery ...
Jianqiang Shen, Lida Li, Weng-Keen Wong
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
14 years 2 months ago
Primal sparse Max-margin Markov networks
Max-margin Markov networks (M3 N) have shown great promise in structured prediction and relational learning. Due to the KKT conditions, the M3 N enjoys dual sparsity. However, the...
Jun Zhu, Eric P. Xing, Bo Zhang
AUSAI
2006
Springer
13 years 11 months ago
Learning Hybrid Bayesian Networks by MML
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
Rodney T. O'Donnell, Lloyd Allison, Kevin B. Korb
UAI
2003
13 years 9 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller