| dc.creator |
Cerquides, Jesus |
|
| dc.creator |
Lopez de Mantaras, Ramon |
|
| dc.date |
2008-03-13T13:29:17Z |
|
| dc.date |
2008-03-13T13:29:17Z |
|
| dc.date |
2005 |
|
| dc.date.accessioned |
2017-01-31T01:00:44Z |
|
| dc.date.available |
2017-01-31T01:00:44Z |
|
| dc.identifier |
Machine Learning: ECML 2005. 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005. Proceedings. Lecture Notes in Artificial Intelligence, Vol. 3720, p.p.: 72-83, Springer Berlin / Heidelberg, 2005. |
|
| dc.identifier |
978-3-540-29243-2 |
|
| dc.identifier |
0302-9743 |
|
| dc.identifier |
http://hdl.handle.net/10261/3221 |
|
| dc.identifier |
10.1007/11564096_12 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3221 |
|
| dc.description |
The original publication is available at
http://www.springerlink.com |
|
| dc.description |
Ensemble classifiers combine the classification results of several classifiers.
Simple ensemble methods such as uniform averaging over a set of models
usually provide an improvement over selecting the single best model. Usually probabilistic
classifiers restrict the set of possible models that can be learnt in order to
lower computational complexity costs. In these restricted spaces, where incorrect
modelling assumptions are possibly made, uniform averaging sometimes performs
even better than bayesian model averaging. Linear mixtures over sets of models provide
an space that includes uniform averaging as a particular case. We develop two
algorithms for learning maximum a posteriori weights for linear mixtures, based on
expectation maximization and on constrained optimization. We provide a nontrivial
example of the utility of these two algorithms by applying them for one dependence
estimators.We develop the conjugate distribution for one dependence estimators and
empirically show that uniform averaging is clearly superior to BMA for this family
of models. After that we empirically show that the maximum a posteriori linear mixture
weights improve accuracy significantly over uniform aggregation. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
144019 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Springer |
|
| dc.rights |
openAccess |
|
| dc.subject |
Artificial Intelligence |
|
| dc.subject |
Bayesian model averaging |
|
| dc.subject |
Averaged One Dependence |
|
| dc.title |
Robust Bayesian Linear Classifier Ensembles |
|
| dc.type |
Artículo |
|