Constrained nonlinear programming
Optimization
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Lazy Learning of Bayesian Rules
Machine Learning
Bayesian Averaging of Classifiers and the Overfitting Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Learning with mixtures of trees
Learning with mixtures of trees
Learning with mixtures of trees
The Journal of Machine Learning Research
Comparing Bayes model averaging and stacking when model approximation error cannot be ignored
The Journal of Machine Learning Research
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Model Averaging for Prediction with Discrete Bayesian Networks
The Journal of Machine Learning Research
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
TAN Classifiers Based on Decomposable Distributions
Machine Learning
Issues in stacked generalization
Journal of Artificial Intelligence Research
Learning mixtures of DAG models
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Efficient lazy elimination for averaged one-dependence estimators
ICML '06 Proceedings of the 23rd international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
Discriminatively Learning Selective Averaged One-Dependence Estimators Based on Cross-Entropy Method
Computational Intelligence and Security
Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators
ECML '07 Proceedings of the 18th European conference on Machine Learning
Anytime learning and classification for online applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Learning Instance-Specific Predictive Models
The Journal of Machine Learning Research
To select or to weigh: a comparative study of model selection and model weighing for SPODE ensembles
ECML'06 Proceedings of the 17th European conference on Machine Learning
Jstacs: a java framework for statistical analysis and classification of biological sequences
The Journal of Machine Learning Research
Credal ensembles of classifiers
Computational Statistics & Data Analysis
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
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Ensemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where incorrect modeling assumptions are possibly made, uniform averaging sometimes performs even better than bayesian model averaging. Linear mixtures over sets of models provide an space that includes uniform averaging as a particular case. We develop two algorithms for learning maximum a posteriori weights for linear mixtures, based on expectation maximization and on constrained optimizition. We provide a nontrivial example of the utility of these two algorithms by applying them for one dependence estimators. We develop the conjugate distribution for one dependence estimators and empirically show that uniform averaging is clearly superior to Bayesian model averaging for this family of models. After that we empirically show that the maximum a posteriori linear mixture weights improve accuracy significantly over uniform aggregation.