Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Different Paradigms for Choosing Sequential Reweighting Algorithms
Neural Computation
Model selection for CART regression trees
IEEE Transactions on Information Theory
Minimax-optimal classification with dyadic decision trees
IEEE Transactions on Information Theory
Mining optimal decision trees from itemset lattices
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Classification Using Geometric Level Sets
The Journal of Machine Learning Research
Optimal constraint-based decision tree induction from itemset lattices
Data Mining and Knowledge Discovery
Combining regular and irregular histograms by penalized likelihood
Computational Statistics & Data Analysis
Journal of Mathematical Imaging and Vision
Risk bounds for CART classifiers under a margin condition
Pattern Recognition
Large margin principle in hyperrectangle learning
Neurocomputing
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We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines guaranteed performance in the learning theoretical sense and optimal search from the algorithmic point of view. Furthermore it inherits the explanatory power of tree approaches, while improving performance over classical approaches such as CART/C4.5, as shown on experiments on artificial and benchmark data.