Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Classifier Learning with Supervised Marginal Likelihood
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Estimating replicability of classifier learning experiments
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A model for weighting image objects in home photographs
Proceedings of the 14th ACM international conference on Information and knowledge management
Classifier hierarchy learning by means of genetic algorithms
Pattern Recognition Letters
Image registration by local histogram matching
Pattern Recognition
On the use of Bayesian Networks to develop behaviours for mobile robots
Robotics and Autonomous Systems
A Bayesian belief network for IT implementation decision support
Decision Support Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Decision Support Systems
A maximum entropy approach to feature selection in knowledge-based authentication
Decision Support Systems
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
Bayesian network modelling through qualitative patterns
Artificial Intelligence
A histogram-based approach for object-based query-by-shape-and-color in image and video databases
Image and Vision Computing
Properties of Bayesian belief network learning algorithms
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Artificial Intelligence in Medicine
Improving dynamic facial expression recognition with feature subset selection
Pattern Recognition Letters
A Bayesian network for burr detection in the drilling process
Journal of Intelligent Manufacturing
Engineering Applications of Artificial Intelligence
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In this work we introduce a methodology based on histogram distances for the automatic induction of Bayesian Networks (BN) from a file containing cases and variables related to a supervised classification problem. The main idea consists of learning the Bayesian Network structure for classification purposes taking into account the classification itself, by comparing the class distribution histogram distances obtained by the Bayesian Network after classifying each case. The structure is learned by applying eight different measures or metrics: the Cooper and Herskovits metric for a general Bayesian Network and seven different statistical distances between pairs of histograms. The results obtained confirm the hypothesis of the authors about the convenience of having a BN structure learning method which takes into account the existence of the special variable (the one corresponding to the class) in supervised classification problems.