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ICML
2007
IEEE
16 years 3 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
IJCAI
2007
15 years 4 months ago
A Theoretical Framework for Learning Bayesian Networks with Parameter Inequality Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
GECCO
2007
Springer
195views Optimization» more  GECCO 2007»
15 years 9 months ago
MILCS: a mutual information learning classifier system
This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifical...
Robert Elliott Smith, Max Kun Jiang
WSC
2004
15 years 4 months ago
Adaptive Wavelet Neural Network for Prediction of Hourly NOx and NO2 Concentrations
Adaptive neural network is a powerful tool for prediction of air pollution abatement scenarios. But it is often difficult to avoid overfit during the training of adaptive neural n...
Zhiguo Zhang, Ye San
CORR
2010
Springer
146views Education» more  CORR 2010»
15 years 3 months ago
Active Learning for Hidden Attributes in Networks
In many networks, vertices have hidden attributes that are correlated with the network's topology. For instance, in social networks, people are more likely to be friends if t...
Xiaoran Yan, Yaojia Zhu, Jean-Baptiste Rouquier, C...