Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification
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...
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS12828_v_n_p_Seijas http://hdl.handle.net/20.500.12110/paper_NIS12828_v_n_p_Seijas |
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paper:paper_NIS12828_v_n_p_Seijas2023-06-08T16:39:34Z Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification Seijas, Leticia María Segura, Enrique Carlos Bayesian statistics Pattern recognition Self-organising maps Ambiguous patterns Bayesian statistics Concordia University Handwritten digit Handwritten numeral Recognition systems Self-organising Unsupervised techniques Conformal mapping Pattern recognition Probability distributions Self organizing maps Pattern recognition systems 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 Concordia University, which is generally accepted as one of the standards in most of the literature in the field. Fil:Seijas, L.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Segura, E.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS12828_v_n_p_Seijas http://hdl.handle.net/20.500.12110/paper_NIS12828_v_n_p_Seijas |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Bayesian statistics Pattern recognition Self-organising maps Ambiguous patterns Bayesian statistics Concordia University Handwritten digit Handwritten numeral Recognition systems Self-organising Unsupervised techniques Conformal mapping Pattern recognition Probability distributions Self organizing maps Pattern recognition systems |
spellingShingle |
Bayesian statistics Pattern recognition Self-organising maps Ambiguous patterns Bayesian statistics Concordia University Handwritten digit Handwritten numeral Recognition systems Self-organising Unsupervised techniques Conformal mapping Pattern recognition Probability distributions Self organizing maps Pattern recognition systems Seijas, Leticia María Segura, Enrique Carlos Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification |
topic_facet |
Bayesian statistics Pattern recognition Self-organising maps Ambiguous patterns Bayesian statistics Concordia University Handwritten digit Handwritten numeral Recognition systems Self-organising Unsupervised techniques Conformal mapping Pattern recognition Probability distributions Self organizing maps Pattern recognition systems |
description |
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 Concordia University, which is generally accepted as one of the standards in most of the literature in the field. |
author |
Seijas, Leticia María Segura, Enrique Carlos |
author_facet |
Seijas, Leticia María Segura, Enrique Carlos |
author_sort |
Seijas, Leticia María |
title |
Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification |
title_short |
Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification |
title_full |
Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification |
title_fullStr |
Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification |
title_full_unstemmed |
Detection of ambiguous patterns in a SOM based recognition system: Application to handwritten numeral classification |
title_sort |
detection of ambiguous patterns in a som based recognition system: application to handwritten numeral classification |
publishDate |
2007 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS12828_v_n_p_Seijas http://hdl.handle.net/20.500.12110/paper_NIS12828_v_n_p_Seijas |
work_keys_str_mv |
AT seijasleticiamaria detectionofambiguouspatternsinasombasedrecognitionsystemapplicationtohandwrittennumeralclassification AT seguraenriquecarlos detectionofambiguouspatternsinasombasedrecognitionsystemapplicationtohandwrittennumeralclassification |
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1769175820917538816 |