A Bayesian Classifier by Using the Adaptive Construct Algorithm of the RBF Networks
| Autor: | Minghu Jiang, Georges Gielen, Beixing Deng, Dafan Liu |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION MathematicsofComputing_NUMERICALANALYSIS Probability density function Pattern recognition Mixture model Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Expectation–maximization algorithm Artificial intelligence business Gradient descent Algorithm |
| Zdroj: | Advances in Neural Networks – ISNN 2004 ISBN: 9783540228417 ISNN (1) |
| DOI: | 10.1007/978-3-540-28647-9_144 |
| Popis: | In paper we propose a Bayesian classifier for multiclass problem by using the merging RBF networks. The estimation of probability density function (PDF) with a Gaussian mixture model is used to update the expectation maximization algorithm. The centers and variances of RBF networks are gradually updated to merge the basis unites by the supervised gradient descent of the error energy function. The algorithms are used to construct the RBF networks and to reduce the number of basis units. The experimental results show the validity of our method which gives a smaller number of basis units and obviously outperforms the conventional RBF learning technique. |
| Databáze: | OpenAIRE |
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