Optimized Learning Vector Quantization Classifier with an Adaptive Euclidean Distance

被引：0

作者：

de Souza, Renata M. C. R. ^{[1
]}

Silva Filho, Telmo de M. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat CIn, BR-50740540 Recife, PE, Brazil

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT I | 2009年 / 5768卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a classifier based on Optimized Learning Vector Quantization (optimized version of the basic LVQ1) and an adaptive Euclidean distance. The classifier furnishes discriminative class regions of the input data set that are represented by prototypes. In order to compare prototypes and patterns, the classifier uses an adaptive Euclidean distance that changes at each iteration but is the same for all the class regions. Experiments with real and synthetic data sets demonstrate the usefulness of this classifier.

引用

页码：799 / 806

页数：8

共 50 条

[1] Admire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization
Michael Biehl
KI - Künstliche Intelligenz, 2012, 26 (4) : 391 - 395
[2] A Novel Learning Vector Quantization Inference Classifier
Maisen, Chakkraphop
Auephanwiriyakul, Sansanee
Theera-Umpon, Nipon
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2017, PT I, 2017, 10191 : 528 - 544
[3] Learning Vector Quantization with Local Subspace Classifier
Hotta, Seiji
19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 2284 - 2287
[4] Distance Learning in Discriminative Vector Quantization
Schneider, Petra
Biehl, Michael
Hammer, Barbara
NEURAL COMPUTATION, 2009, 21 (10) : 2942 - 2969
[5] Admire LVQ-Adaptive Distance Measures in Relevance Learning Vector Quantization
Biehl, Michael
KUNSTLICHE INTELLIGENZ, 2012, 26 (04): : 391 - 395
[6] A classification model based on Support Vector Machines and Euclidean distance classifier
Zhu Yonli
Zheng Jianbai
Wang Fang
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE INFORMATION COMPUTING AND AUTOMATION, VOLS 1-3, 2008, : 609 - +
[7] Evolutionary generation of prototypes for a learning vector quantization classifier
Cordella, L. P.
De Stefano, C.
Fontanella, F.
Marcelli, A.
APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2006, 3907 : 391 - 402
[8] Rate distortion optimized adaptive multiscale vector quantization
de Carvalho, MB
da Silva, EAB
Finamore, WA
2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, 2001, : 439 - 442
[9] A fast encoding method for vector quantization by using multi Euclidean distance estimations
Pan, ZB
Ohmi, T
Kotani, K
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2004, 10 (02): : 167 - 174
[10] Learning vector quantization inference classifier in breast abnormality classification
Maisen, Chakkraphop
Auephanwiriyakul, Sansanee
Theera-Umpon, Nipon
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (06) : 6101 - 6116

← 1 2 3 4 5 →