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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Frosyniotis, D. | en |
| dc.contributor.author | Stafylopatis, A. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:00:22Z | - |
| dc.date.available | 2015-11-24T17:00:22Z | - |
| dc.identifier.issn | 1433-7541 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10755 | - |
| dc.rights | Default Licence | - |
| dc.subject | classifier combination | en |
| dc.subject | classifier fusion | en |
| dc.subject | clustering | en |
| dc.subject | divide-and-conquer | en |
| dc.subject | multiple classifier systems | en |
| dc.title | A divide-and-conquer method for multi-net classifiers | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1007/s10044-002-0174-6 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2003 | - |
| heal.abstract | Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a pattern classification multi-net system based on both supervised and unsupervised learning. Following the 'divide-and-conquer' framework, the input space is partitioned into overlapping subspaces and neural networks are subsequently used to solve the respective classification subtasks. Finally, the outputs of individual classifiers are appropriately combined to obtain the final classification decision. Two clustering methods have been applied for input space partitioning and two schemes have been considered for combining the outputs of the multiple classifiers. Experiments on well-known data sets indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel). | en |
| heal.journalName | Pattern Analysis and Applications | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Frosyniotis-2003-A divide-and-conquer.pdf | 182.7 kB | Adobe PDF | View/Open Request a copy |
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