Widened Learning of Bayesian Network Classifiers
| Autor: | Oliver R. Sampson, Michael R. Berthold |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Markov blanket
Computer science business.industry Matrix norm Bayesian network 02 engineering and technology Space (commercial competition) Machine learning computer.software_genre Measure (mathematics) ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Laplacian matrix ddc:004 Greedy algorithm Minimum description length business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319463483 IDA |
| Popis: | We demonstrate the application of Widening to learning performant Bayesian Networks for use as classifiers. Widening is a framework for utilizing parallel resources and diversity to find models in a hypothesis space that are potentially better than those of a standard greedy algorithm. This work demonstrates that widened learning of Bayesian Networks, using the Frobenius Norm of the networks’ graph Laplacian matrices as a distance measure, can create Bayesian networks that are better classifiers than those generated by popular Bayesian Network algorithms. published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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