a stochastic competitive learning vector quantization algorithm for image coding 一种随机竞争学习矢量量化图像编码算法
the purpose of this study was to explore the effect of the different training samples, learning rate and numbers of hidden layers on the classified accuracy when using learning vector quantization to analysis 摘要本研究采电脑模拟探讨不同训练范例样本数、学习速率及隐藏层个数对学习向量量化网路分类正确率之影响。
the purpose of this study was to explore the effect of the different training samples, learning rate and numbers of hidden layers on the classified accuracy when using learning vector quantization to analysis 摘要本研究采计算机仿真探讨不同训练示范样本数、学习速率及隐藏层个数对学习矢量量化网络分类正确率之影响。
this method combines a genetic algorithm with an artificial neural network classifier, such as back-propagation ( bp ) neural classifier, radial basis function ( rbf ) classifier or learning vector quantization ( lvq ) classifier 此方法结合基因演算法与类神经分类器,如倒传递分类器、放射基底函数分类器以及学习矢量量化分类器。
secondly, a multilayered neural network trained with a learning vector quantization ( lvq ) algorithm is applied to pattern recognition of manifestations of the pulse and the classification ability of lvq network is compared with traditional near neighbor algorithm 其次,本文根据脉图的时域特征,采用学习矢量量化算法,训练文中确立的神经网络分类器,用以实现对脉图的识别。并比较了lvq神经网络分类器与传统近邻法的分类性能。