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.

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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.

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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.

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