Inferring decision trees using the minimum description length principle
Information and Computation
An incremental method for finding multivariate splits for decision trees
Proceedings of the seventh international conference (1990) on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Convergence Results for the EM Approach to Mixtures of Experts Architectures
Convergence Results for the EM Approach to Mixtures of Experts Architectures
An Introduction to Variational Methods for Graphical Models
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Investigation and Reduction of Discretization Variance in Decision Tree Induction
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Visual development and the acquisition of motion velocity sensitivities
Neural Computation
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Closed-form dual perturb and combine for tree-based models
ICML '05 Proceedings of the 22nd international conference on Machine learning
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A statistical approach to decision tree modeling is described. In this approach, each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions. The resulting model yields a likelihood measure of goodness of fit, allowing ML and MAP estimation techniques to be utilized. An efficient algorithm is presented to estimate the parameters in the tree. The model selection problem is presented and several alternative proposals are considered. A hidden Markov version of the tree is described for data sequences that have temporal dependencies.