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https://olympias.lib.uoi.gr/jspui/handle/123456789/10907Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lagaris, I. E. | en |
| dc.contributor.author | Voglis, C | en |
| dc.date.accessioned | 2015-11-24T17:01:19Z | - |
| dc.date.available | 2015-11-24T17:01:19Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10907 | - |
| dc.rights | Default Licence | - |
| dc.subject | Arti?cial neural networks, | en |
| dc.subject | global optimization, | en |
| dc.subject | multistart. | en |
| dc.title | A Global Optimization Approach to Neural Network Training | 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 | 2006 | - |
| heal.abstract | We study effective approaches for training arti?cial neural networks (ANN). We argue that local optimization methods by themselves are not suited for that task. In fact we show that global optimization methods are absolutely necessary if the training is required to be robust. This is so because the objective function under consideration possesses a multitude of minima while only a few may correspond to acceptable solutions that generalize well. | en |
| heal.journalName | Neural Parallel and Scientific Computations | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Lagaris-2006-A Global Optimization Approach to.pdf | 106.5 kB | Adobe PDF | View/Open Request a copy |
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