Scaling MLN weights by 1/n induces a weight-independent 0-1 law for FO logic; unscaled weights produce seven regimes with possible phase transitions and convergence laws.
2009.Probabilistic Graphical Models: Principles and Techniques
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Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.
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Random coloured digraphs defined by a Markov logic network
Scaling MLN weights by 1/n induces a weight-independent 0-1 law for FO logic; unscaled weights produce seven regimes with possible phase transitions and convergence laws.
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Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.