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
우리는 많은 카테고리를 포함하는 대규모 분류 문제에 대해 HLVQ(Hierarchical Learning Vector Quantization)를 사용한 대략적인 분류 시스템을 제안합니다. 제안된 시스템의 HLVQ는 특징 공간에서 범주를 계층적으로 나누고 트리를 만들고 계층 아래로 노드를 곱합니다. 특징 공간은 각 레이어의 몇 개의 코드북 벡터로 나뉩니다. 인접한 특징 공간은 경계에서 겹칩니다. HLVQ 분류는 계층적 아키텍처와 중첩 기술로 인해 빠르고 정확합니다. 일본 최대 규모의 손글씨 데이터베이스인 ETL9B(607,200개 카테고리, 총 3036개의 샘플 포함)를 이용한 분류 실험에서 HLVQ에 의한 분류 속도와 정확도가 Self-Organizing Feature Map에 비해 높은 것으로 나타났습니다( SOM) 및 학습 벡터 양자화 방법. HLVQ에 따라 카테고리별 다중 코드북 벡터를 사용하는 제안 시스템의 분류율이 평균 벡터를 사용하는 시스템보다 더 높은 속도와 정확도를 얻을 수 있음을 입증한다.
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Yuji WAIZUMI, Nei KATO, Kazuki SARUTA, Yoshiaki NEMOTO, "High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 6, pp. 1282-1290, June 2000, doi: .
Abstract: We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_6_1282/_p
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@ARTICLE{e83-d_6_1282,
author={Yuji WAIZUMI, Nei KATO, Kazuki SARUTA, Yoshiaki NEMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization},
year={2000},
volume={E83-D},
number={6},
pages={1282-1290},
abstract={We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.},
keywords={},
doi={},
ISSN={},
month={June},}
부
TY - JOUR
TI - High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization
T2 - IEICE TRANSACTIONS on Information
SP - 1282
EP - 1290
AU - Yuji WAIZUMI
AU - Nei KATO
AU - Kazuki SARUTA
AU - Yoshiaki NEMOTO
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2000
AB - We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.
ER -