Tractability of most probable explanations in multidimensional Bayesian network classifiers
DOI10.1016/j.ijar.2017.10.024zbMath1452.68146OpenAlexW2766664691WikidataQ62637334 ScholiaQ62637334MaRDI QIDQ1726375
Concha Bielza, Pedro Larrañaga, Marco Benjumeda
Publication date: 20 February 2019
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Full work available at URL: http://oa.upm.es/54551/
machine learningBayesian network classifiersmultidimensional classificationmost probable explanation complexity
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Most probable explanations in Bayesian networks: complexity and tractability
- Multi-dimensional classification with Bayesian networks
- A scalable pairwise class interaction framework for multidimensional classification
- Tree induction for probability-based ranking
- Maximum cardinality search for computing minimal triangulations of graphs
- Treewidth computation and extremal combinatorics
- The Elimination form of the Inverse and its Application to Linear Programming
- On exact algorithms for treewidth
- Trading off Speed and Accuracy in Multilabel Classification
- Multi-classifiers of Small Treewidth
- Inference and Learning in Multi-dimensional Bayesian Network Classifiers
- Modified Sequentially Rejective Multiple Test Procedures
- Complexity of Finding Embeddings in a k-Tree
- Algorithmic Aspects of Vertex Elimination on Graphs
- Discrete Bayesian Network Classifiers
- A linear time algorithm for finding tree-decompositions of small treewidth
- Machine Learning: ECML 2004
- Heuristic and metaheuristic methods for computing graph treewidth
This page was built for publication: Tractability of most probable explanations in multidimensional Bayesian network classifiers
