Learning graphical models with hypertree structure using a simulated annealing approach

IEEE International Conference on Fuzzy Systems

Volumn 1, Issue , 2001, Pages 135-138

  Borgelt, Christian ^a   Kruse, Rudolf ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; GRAPHIC METHODS; MATHEMATICAL MODELS; SET THEORY; SIMULATED ANNEALING;

GRAPHICAL MODELS; HYPERTREE STRUCTURE;

LEARNING ALGORITHMS;

EID: 0035746884     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

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2. Some experimental results on learning probabilistic and possibilistic networks with different evaluation measures
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4. Learning possibilistic networks from data
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6. Optimization by simulated annealing
7. Local computations with probabilities on graphical structures and their application to expert systems
8. Equation of state calculations for fast computing machines
9. Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs and selectively reduce acyclic hypergraphs