Class conditional density estimation using mixtures with constrained component sharing
| dc.contributor.author | Titsias, M. K. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:00:32Z | |
| dc.date.available | 2015-11-24T17:00:32Z | |
| dc.identifier.issn | 0162-8828 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10776 | |
| dc.rights | Default Licence | - |
| dc.subject | mixture models | en |
| dc.subject | classification | en |
| dc.subject | density estimation | en |
| dc.subject | em algorithm | en |
| dc.subject | component sharing | en |
| dc.subject | em algorithm | en |
| dc.subject | maximum-likelihood | en |
| dc.title | Class conditional density estimation using mixtures with constrained component sharing | en |
| heal.abstract | We propose a generative mixture model classifier that allows for the class conditional densities to be represented by mixtures having certain subsets of their components shared or common among classes. We argue that, when the total number of mixture components is kept fixed, the most efficient classification model is obtained by appropriately determining the sharing of components among class conditional densities. In order to discover such an efficient model, a training method is derived based on the EM algorithm that automatically adjusts component sharing. We provide experimental results with good classification performance. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.journalName | Ieee Transactions on Pattern Analysis and Machine Intelligence | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2003 | - |
| 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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