learning vector quantization

基本解释

Neurons can not be taken to full advantage and the information between feature vectors and neurons is wasted among supervised learning vector quantization (LVQ)neural networks.
针对监督LVQ神经网络存在神经元未被充分利用以及输入样本和竞争单元之间的信息被浪费等问题。
The principle of fuzzy vector quantization (FVQ) for image coding is discussed in this paper, and an exponential fuzzy learning vector quantization algorithm (EFLVQ) is proposed.
本文分析了模糊矢量量化（FVQ）图像编码的原理，提出了一种指数型模糊学习矢量量化算法（EFLVQ）。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。