Bill money classification by competitive learning

T. Kosaka, S. Omatu
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引用次数: 10

Abstract

The progress of computer science enables us to process complex and large scale computations and advanced pattern recognition methods can be adopted for pattern classification problems. Among them neuro-pattern recognition, which means pattern recognition based on neural networks, has been given attention since it has classified various patterns like human beings. We adopt the learning vector quantization (LVQ) method to classify money. The reasons for using the LVQ are that it can process unsupervised classification data and treat a large amount of input data with a small computational burden. We construct the LVQ network to classify Italian Lira. Compared with a conventional pattern matching technique, which has been adopted as a classification method, the proposed method has shown excellent classification results.
通过竞争学习对票据货币进行分类
计算机科学的进步使我们能够处理复杂和大规模的计算,并且可以采用先进的模式识别方法来解决模式分类问题。其中神经模式识别(neural -pattern recognition),即基于神经网络的模式识别,由于能像人类一样对多种模式进行分类而备受关注。我们采用学习向量量化(LVQ)方法对货币进行分类。使用LVQ的原因是它可以处理无监督分类数据,并且可以用很小的计算负担处理大量的输入数据。我们构建LVQ网络对意大利里拉进行分类。与传统的模式匹配分类方法相比,该方法具有较好的分类效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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