Active learning with the probabilistic RBF classifier
| dc.contributor.author | Constantinopoulos, C. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:01:15Z | |
| dc.date.available | 2015-11-24T17:01:15Z | |
| dc.identifier.issn | 0302-9743 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10896 | |
| dc.rights | Default Licence | - |
| dc.subject | mixture model | en |
| dc.subject | algorithm | en |
| dc.title | Active learning with the probabilistic RBF classifier | en |
| heal.abstract | In this work we present an active learning methodology for training the probabilistic RBF (PRBF) network. It is a special case of the RBF network, and constitutes a generalization of the Gaussian mixture model. We propose an incremental method for semi-supervised learning based on the Expectation-Maximization (EM) algorithm. Then we present an active learning method that iteratively applies the semi-supervised method for learning the labeled and unlabeled observations concurrently, and then employs a suitable criterion to select an unlabeled observation and query its label. The proposed criterion selects points near the decision boundary, and facilitates the incremental semi-supervised learning that also exploits the decision boundary. The performance of the algorithm in experiments using well-known data sets is promising. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.journalName | Artificial Neural Networks - Icann 2006, | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2006 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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