| dc.creator |
Cerquides, Jesus |
|
| dc.creator |
Lopez de Mantaras, Ramon |
|
| dc.date |
2008-02-21T13:05:36Z |
|
| dc.date |
2008-02-21T13:05:36Z |
|
| dc.date |
2005 |
|
| dc.date.accessioned |
2017-01-31T01:00:18Z |
|
| dc.date.available |
2017-01-31T01:00:18Z |
|
| dc.identifier |
Machine Learning, 2005, 59 (3): 323-354 |
|
| dc.identifier |
0885-6125 |
|
| dc.identifier |
http://hdl.handle.net/10261/3019 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3019 |
|
| dc.description |
The original publication is available at www.springerlink.com |
|
| dc.description |
In this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN's, we can compute the exact Bayesian model averaging over TAN structures and parameters in polynomial time. Furthermore, we prove that the k-maximum a posteriori (MAP) TAN structures can also be computed in polynomial time. We use these results to correct minor errors in Meila & Jaakkola (2000a) and to construct several TAN based classifiers provide consistently better predictions over Irvine datasets and artificially generated data than TAN based classifiers proposed in the literature. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
426079 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Springer |
|
| dc.rights |
openAccess |
|
| dc.subject |
Artificial Intelligence |
|
| dc.subject |
Bayesian networks classifiers |
|
| dc.subject |
Naive Bayes |
|
| dc.subject |
Tree augmented naive Bayes |
|
| dc.subject |
Decomposable distributions |
|
| dc.subject |
Bayesian model averaging |
|
| dc.title |
TAN Classifiers Based on Decomposable Distributions |
|
| dc.type |
Artículo |
|