提出利用主元分析（PCA）和学习矢量量化神经网络（LVQ）相结合的方法进行人脸识别。
This paper proposes a face recognition method based on PCA and LVQ neural networks.
将学习矢量量化神经网络集成在基于实例推理的故障诊断方法中，减小了实例搜索空间，提高了实例检索效率。
The learning vector quantization neural network has been integrated successfully with the case-based reasoning approach to reduce the case indexing space and to enhance the indexing efficiency.
对向传播神经网络(CPN)可以作为矢量量化器用于图像压缩，但CPN学习算法在进行码书设计时存在两个明显的缺陷。
The Counterpropagation Network (CPN) can be applied to image compression as a vector quantizer. However, the CPN learning algorithm has two obvious disadvantages in codebook designing.
目的：探讨学习矢量量化(LVQ)人工神经网络在伤寒、副伤寒发生强度判别与预测中的应用。
Objective: to investigate the potential of learning vector quantization (LVQ) artificial neural network tools for discrimination and forecasting of occurrent intensity of typhoid and paratyphoid.
比较而言，学习矢量量化网络在分类能力和训练成本方面更胜一筹，而概率神经网络则在计算负载和易用性方面更好一些。
By comparison, LVQ network was better than the others in classification ability and training cost, and PNN network in computation load and easy use.
仍然有些算法很容易就可以被归入好几个类别，好比学习矢量量化，它既是受启发于神经网络的方法，又是基于实例的方法。
There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method.
本文基于学习矢量量化（LVQ）神经网络分类器，实现了舌象分析中的舌色、苔色自动分类。
Tongue color automatic classification, based on LVQ neural networks classifier, is proposed in this paper.
为有效提高矢量量化码书的性能和学习效率，需进一步改进自组织神经网络的学习算法。
Self-organizing neural network is a very efficient method for pattern recognition and vector quantization(VQ).
比较而言，学习矢量量化网络和概率神经网络在分类能力方面要比反向传播网络好一些，概率神经网络在计算负载方面比学习矢量量化网络要更胜一筹。
By comparison, LVQ network and PNN network are better than BPN network in classification ability, and PNN network is better than the others in computation load.
比较而言，学习矢量量化网络和概率神经网络在分类能力方面要比反向传播网络好一些，概率神经网络在计算负载方面比学习矢量量化网络要更胜一筹。
By comparison, LVQ network and PNN network are better than BPN network in classification ability, and PNN network is better than the others in computation load.
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