Universität Bielefeld Electronic Collections animiertes Foto Universität Bielefeld

Zugang zum Dokument



Detection of ambiguous patterns in a SOM based recognition system: application to handwritten numeral classification

Seijas, Leticia Maria ; Segura, Enrique Carlos




Abstract:
This work presents a system for pattern recognition that combines a self-organising unsupervised technique (via a Kohonen-type SOM) with a bayesian strategy in order to classify input patterns from a given probability distribution and, at the same time, detect ambiguous cases and explain answers. We apply the system to the recognition of handwritten digits. This proposal is intended as an improvement of a model previously introduced by our group, consisting basically of a hybrid unsupervised, self-organising model, followed by a supervised stage. Experiments were carried out on the handwritten digit database of the Concordia University, which is generally accepted as one of the standards in most of the literature in the field.


Schlagwörter: self-organising maps, pattern recognition, bayesian statistics
Beteiligte Einrichtung: Technische Fakultät, Arbeitsgruppen der Informatik
DDC-Sachgruppe: Datenverarbeitung, Informatik

Zitat-Vorschlag:
Seijas, Leticia Maria ; Segura, Enrique Carlos  (2007)  Detection of ambiguous patterns in a SOM based recognition system: application to handwritten numeral classification.


URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/161



 Fragen und Anregungen an: publikationsdienste.ub@uni-bielefeld.de
 Letzte Änderung: 15.2.2011
 Impressum
OPUS-Logo     OAI-zertifiziert      Universitätsbibliothek Bielefeld
OAI-Logo