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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Constantinopoulos, C. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:00:37Z | - |
| dc.date.available | 2015-11-24T17:00:37Z | - |
| dc.identifier.issn | 0302-9743 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10791 | - |
| dc.rights | Default Licence | - |
| dc.subject | machine learning | en |
| dc.subject | neural networks | en |
| dc.subject | probabilistic reasoning | en |
| dc.subject | mixture models | en |
| dc.subject | classification | en |
| dc.subject | em algorithm | en |
| dc.title | Efficient training algorithms for the probabilistic RBF network | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2004 | - |
| heal.abstract | The Probabilistic RBF (PRBF) network constitutes an adaptation of the RBF network for classification. Moreover it extends the typical mixture model by allowing the sharing of mixture components among all classes, in contrast to the conventional approach that suggests mixture components describing only one class. The typical learning method of PRBF for a classification task employs the Expectation - Maximization (EM) algorithm. This widely used method depends strongly on the initial parameter values. The Greedy EM algorithm is a recently proposed method that tries to overcome this drawback, in the case of the density estimation problem using mixture models. In this work we propose a similar approach for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components. After convergence the algorithm splits all the components of the network. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. Experimental results using several well-known classification datasets indicate that the incremental method provides solutions of superior classification performance. | en |
| heal.journalName | Methods and Applications of Artificial Intelligence, Proceedings | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Likas-2004-Efficient training algorithms for the probabilistic RBF network.pdf | 265.29 kB | Adobe PDF | View/Open Request a copy |
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