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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Constantinopoulos, C. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:01:46Z | - |
| dc.date.available | 2015-11-24T17:01:46Z | - |
| dc.identifier.issn | 0925-2312 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10974 | - |
| dc.rights | Default Licence | - |
| dc.subject | probabilistic rbf network | en |
| dc.subject | active learning | en |
| dc.subject | semi-supervised learning | en |
| dc.subject | em algorithm | en |
| dc.subject | unlabeled data | en |
| dc.subject | mixture model | en |
| dc.subject | em algorithm | en |
| dc.title | Semi-supervised and active learning with the probabilistic RBF classifier | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.neucom.2007.11.039 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2008 | - |
| heal.abstract | The probabilistic RBF network (PRBF) is a special case of the RBF network and constitutes a generalization of the Gaussian mixture model. In this paper we propose a semi-supervised learning method for PRBF, using labeled and unlabeled observations concurrently, that is based on the expectation-maximization (EM) algorithm. Next we utilize this method in order to implement an incremental active learning method. At each iteration of active learning, we apply the semi-supervised method, and then we employ a criterion to select an unlabeled observation and query its label. This criterion identifies points near the decision boundary. In order to assess the effectiveness of our method, we propose an adaptation of the well-known Query by Committee (QBC) algorithm for the active learning of the PBFR, and present experimental comparisons on several data sets that indicate the effectiveness of the proposed method. (C) 2008 Elsevier B.V. All rights reserved. | en |
| heal.journalName | Neurocomputing | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Constantinopoulos-2008-Semi-supervised and.pdf | 760.74 kB | Adobe PDF | View/Open Request a copy |
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