Mélanges sous-quadratiques d'arbres de Markov pour l'estimation de la densité de probabilité
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Summary
Mixtures of tree structured posed heuristics whose complexity is sub-quadratic outperform other random linear methods and approache the quadratic baseline method when the number of mixture components grows.
- Type
- preprint
- Published
- 2010-05-01
- Cited by
- 1
- References
- 10
- Access
- Open access
- OpenAlex
- https://openalex.org/W36147656
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:115907199
Keywords
Mathematics, Humanities, Philosophy
References
- On Information and Sufficiency
- High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks
- Probabilistic Networks and Expert Systems
- A minimum spanning tree algorithm with inverse-Ackermann type complexity
- Extremely randomized trees
- Looking for lumps: boosting and bagging for density estimation
- Approximating discrete probability distributions with dependence trees
- Probability Density Estimation by Perturbing and Combining Tree Structured Markov Networks
- Fusion, Propagation, and Structuring in Belief Networks
- Introduction à l'algorithmique
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