The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous présentons une classe de classificateurs combinatoires-logiques appelés classificateurs de fonctionnalités de test. Ce sont des fonctions polynomiales qui peuvent être utilisées comme classificateurs de modèles de vecteurs de caractéristiques à valeurs binaires. La méthode est basée sur ce que l'on appelle des tests, des ensembles de caractéristiques, qui sont suffisants pour distinguer les modèles de différentes classes d'échantillons d'apprentissage. Basé sur le concept de test, nous proposons un nouveau classificateur de fonctionnalités de test basé sur la distance. Pour tester les performances des classificateurs, nous les appliquons à une base de données de phonèmes bien connue et à un problème de localisation de régions textuelles où nous proposons un nouveau système de recherche de régions textuelles efficace capable de localiser des régions textuelles dans un arrière-plan complexe. Les résultats expérimentaux montrent que les classificateurs proposés donnent un taux de reconnaissance plus élevé que les classificateurs conventionnels, ont une grande capacité de généralisation et suggèrent qu'ils peuvent être utilisés dans diverses applications de reconnaissance de formes.
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Vakhtang LASHKIA, Shun'ichi KANEKO, Stanislav ALESHIN, "Distance-Based Test Feature Classifiers and Its Applications" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 4, pp. 904-913, April 2000, doi: .
Abstract: In this paper, we present a class of combinatorial-logical classifiers called test feature classifiers. These are polynomial functions that can be used as pattern classifiers of binary-valued feature vectors. The method is based on so-called tests, sets of features, which are sufficient to distinguish patterns from different classes of training samples. Based on the concept of test we propose a new distance-based test feature classifiers. To test the performance of the classifiers, we apply them to a well-known phoneme database and to a textual region location problem where we propose a new effective textual region searching system that can locate textual regions in a complex background. Experimental results show that the proposed classifiers yield a high recognition rate than conventional ones, have a high ability of generalization, and suggest that they can be used in a variety of pattern recognition applications.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_4_904/_p
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@ARTICLE{e83-d_4_904,
author={Vakhtang LASHKIA, Shun'ichi KANEKO, Stanislav ALESHIN, },
journal={IEICE TRANSACTIONS on Information},
title={Distance-Based Test Feature Classifiers and Its Applications},
year={2000},
volume={E83-D},
number={4},
pages={904-913},
abstract={In this paper, we present a class of combinatorial-logical classifiers called test feature classifiers. These are polynomial functions that can be used as pattern classifiers of binary-valued feature vectors. The method is based on so-called tests, sets of features, which are sufficient to distinguish patterns from different classes of training samples. Based on the concept of test we propose a new distance-based test feature classifiers. To test the performance of the classifiers, we apply them to a well-known phoneme database and to a textual region location problem where we propose a new effective textual region searching system that can locate textual regions in a complex background. Experimental results show that the proposed classifiers yield a high recognition rate than conventional ones, have a high ability of generalization, and suggest that they can be used in a variety of pattern recognition applications.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Distance-Based Test Feature Classifiers and Its Applications
T2 - IEICE TRANSACTIONS on Information
SP - 904
EP - 913
AU - Vakhtang LASHKIA
AU - Shun'ichi KANEKO
AU - Stanislav ALESHIN
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2000
AB - In this paper, we present a class of combinatorial-logical classifiers called test feature classifiers. These are polynomial functions that can be used as pattern classifiers of binary-valued feature vectors. The method is based on so-called tests, sets of features, which are sufficient to distinguish patterns from different classes of training samples. Based on the concept of test we propose a new distance-based test feature classifiers. To test the performance of the classifiers, we apply them to a well-known phoneme database and to a textual region location problem where we propose a new effective textual region searching system that can locate textual regions in a complex background. Experimental results show that the proposed classifiers yield a high recognition rate than conventional ones, have a high ability of generalization, and suggest that they can be used in a variety of pattern recognition applications.
ER -