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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 테스트 특징 분류기라고 불리는 조합 논리 분류기 클래스를 제시합니다. 이는 이진 값 특징 벡터의 패턴 분류자로 사용할 수 있는 다항식 함수입니다. 이 방법은 다양한 클래스의 훈련 샘플에서 패턴을 구별하는 데 충분한 기능 세트인 소위 테스트를 기반으로 합니다. 테스트 개념을 기반으로 새로운 거리 기반 테스트 기능 분류기를 제안합니다. 분류기의 성능을 테스트하기 위해 우리는 이를 잘 알려진 음소 데이터베이스와 텍스트 영역 위치 문제에 적용하여 복잡한 배경에서 텍스트 영역을 찾을 수 있는 새롭고 효과적인 텍스트 영역 검색 시스템을 제안합니다. 실험 결과, 제안된 분류기는 기존 분류기보다 인식률이 높고, 일반화 능력이 높으며, 다양한 패턴 인식 응용 분야에 활용될 수 있음을 보여줍니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -