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IJAR
2010
130views more  IJAR 2010»
13 years 10 months ago
Learning locally minimax optimal Bayesian networks
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Tomi Silander, Teemu Roos, Petri Myllymäki
IJIS
2006
115views more  IJIS 2006»
13 years 11 months ago
Noisy-or classifier
We discuss an application of a family of Bayesian network models
Jirí Vomlel
UAI
1993
14 years 27 days ago
Using Causal Information and Local Measures to Learn Bayesian Networks
In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL ...
Wai Lam, Fahiem Bacchus
IJCAI
2003
14 years 28 days ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
FLAIRS
2006
14 years 29 days ago
Modeling Bayesian Networks for Autonomous Diagnosis of Web Services
We took an innovative approach to service level management for network enterprise systems by using integrated monitoring, diagnostics, and adaptation services in a service-oriente...
Haiqin Wang, Guijun Wang, Alice Chen, Changzhou Wa...
SIGMOD
2006
ACM
123views Database» more  SIGMOD 2006»
14 years 11 months ago
Proactive identification of performance problems
We propose to demonstrate Fa, an automated tool for timely and accurate prediction of Service-Level-Agreement (SLA) violations caused by performance problems in database systems. ...
Songyun Duan, Shivnath Babu
ICML
1996
IEEE
15 years 11 days ago
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian ne...
Kazuo J. Ezawa, Moninder Singh, Steven W. Norton