Name
Abstract | ||
|---|---|---|
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods such as maximization of the joint likelihood. The reason is often that it is unclear how to find the parameters maximizing the conditional (supervised) likelihood. We show how the discriminative learning problem can be solved efficiently for a large class of Bayesian network models, including the Naive Bayes (NB) and tree-augmented Naive Bayes (TAN) models. We do this by showing that under a certain general condition on the network structure, the discriminative learning problem is exactly equivalent to logistic regression with unconstrained convex parameter spaces. Hitherto this was known only for Naive Bayes models. Since logistic regression models have a concave log-likelihood surface, the global maximum can be easily found by local optimization methods. |
Year | Venue | Keywords |
|---|---|---|
2003 | IJCAI | network structure,certain general condition,discriminative learning,tree-augmented naive bayes,bayesian network parameter,discriminative prediction task,bayesian network model,concave log-likelihood surface,naive bayes model,logistic regression model,joint likelihood,naive bayes,bayesian network,discrimination learning,parameter space |
Field | DocType | Citations |
Variable-order Bayesian network,Naive Bayes classifier,Pattern recognition,Computer science,Bayes factor,Supervised learning,Bayesian network,Bayesian programming,Artificial intelligence,Discriminative model,Maximization,Machine learning | Conference | 10 |
PageRank | References | Authors |
0.81 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hannes Wettig | 1 | 108 | 9.08 |
| Peter Grunwald | 2 | 113 | 11.40 |
| Teemu Roos | 3 | 436 | 61.32 |
| Petri Myllymaki | 4 | 69 | 9.84 |
| Henry Tirri | 5 | 691 | 145.38 |