Image recognition by using generalized learning vector quantization

被引：0
|
作者：
Sato A.
机构：
来源：
Sato, Atsushi | 1600年 / Japan Society for Precision Engineering卷 / 83期
关键词：
Bayes decision theory; Image recognition; Loss Minimization; Machine learning; Vector Quantization;
D O I：
10.2493/jjspe.83.335
中图分类号：
学科分类号：
摘要：
This paper describes image recognition by using Generalized Learning Vector Quantization (GLVQ). GLVQ has been proposed as a learning method of reference vectors that ensures convergence of them during learning. By formulating a novel learning scheme called General Loss Minimization (GLM) based on Bayes decision theory, GLVQ can be extended to various discrimination methods including Inverse of Lorentzian Mixture (1LM) and Discriminative Dimensionality Reduction (DDR). Application to motorcycle recognition reveals that the proposed method work well in the real world, and it contributes to the achievement of safe and secure societies.
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页码：335 / 340
页数：5
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