学习矢量量化网络(Learning Vector Quantization,LVQ)是一种混和网络，由输入层、竞争层和 线性输出层组成。通过有监督及无监督的学习进行分类。
基于16个网页-相关网页
在Kohonen提出的学习矢量量化网络（Learning Vector Quantization Network,LVQ）的基础上,引入阈值学习规则,较好地解决了该类网络中遇到“死”点时训练误差偏大的问题,最后通过Matl...
基于8个网页-相关网页
...器学习方法作为建立模型的训练算法,分别为反向传播(Back Propagation,BP)神经网络、学习矢量量化网络(Learning Vector Quantity,LVQ)和支持向量机(Support Vector Machine,SVM)。仿真结果显示.
基于2个网页-相关网页
学习矢量量化神经网络 LVQNN
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.
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.
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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