Name
Title | ||
|---|---|---|
Efficient Approximation of the Conditional Relative Entropy with Applications to Discriminative Learning of Bayesian Network Classifiers. |
Abstract | ||
|---|---|---|
We propose a minimum variance unbiased approximation to the conditional relative entropy of the distribution induced by the observed frequency estimates, for multi-classification tasks. Such approximation is an extension of a decomposable scoring criterion, named approximate conditional log-likelihood (aCLL), primarily used for discriminative learning of augmented Bayesian network classifiers. Our contribution is twofold: (i) it addresses multi-classification tasks and not only binary-classification ones; and (ii) it covers broader stochastic assumptions than uniform distribution over the parameters. Specifically, we considered a Dirichlet distribution over the parameters, which was experimentally shown to be a very good approximation to CLL. In addition, for Bayesian network classifiers, a closed-form equation is found for the parameters that maximize the scoring criterion. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3390/e15072716 | ENTROPY |
Keywords | Field | DocType |
conditional relative entropy,approximation,discriminative learning,Bayesian network classifiers | Minimum-variance unbiased estimator,Pattern recognition,Uniform distribution (continuous),Bayesian network,Artificial intelligence,Dirichlet distribution,Statistics,Discriminative model,Kullback–Leibler divergence,Mathematics,Discriminative learning | Journal |
Volume | Issue | Citations |
15 | 7 | 5 |
PageRank | References | Authors |
0.49 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandra M. Carvalho | 1 | 223 | 16.39 |
| Pedro Adão | 2 | 103 | 7.33 |
| Paulo Mateus | 3 | 33 | 4.55 |