1) learning vector quantization
学习向量量化
1.
Recognition of cancerous stomach tissues by artificial learning vector quantization neural network;
学习向量量化神经网络用于胃癌组织样品分类识别的研究
2.
Based on statistics and analysis of three approaches for document clustering,the c-means,the fuzzy c-means and the learning vector quantization approach,this paper transfers some ideas from fuzzy clustering,in particular the use of a covariance matrix to describe the shape and the size of a cluster,to learning vector quantization.
基于对文献聚类的3种方法(c-means法、模糊c-means法和学习向量量化法)的统计和分析,借鉴了模糊聚类思想,尤其是用协方差矩阵来描述聚类的形状和大小,并将其应用于学习向量量化算法中。
3.
In this paper,the principle of ANN classifier is introduced firstly,and then learning vector quantization(LVQ)of ANN is used in the speaker identification experiment with the assistance of Matlab,which satisfying results are obtained eventually.
本文在介绍人工神经网络实现对输入向量分类原理的基础上,通过MATLAB实现了基于神经网络学习向量量化方法(LVQ)的说话人识别实验,取得了较为满意的结果。
2) LVQ
学习向量量化
1.
An Anti-spam E-mail Filter Based on LVQ Network;
一种基于学习向量量化网络的垃圾邮件过滤方法
2.
A Method of Recognizing Objects Specified by Color Based on LVQ Neural Network;
基于学习向量量化网络的指定颜色物体的识别
3) Learning Vector
学习向量
1.
An Application of Learning Vector Neural Network to Image Interpolation
一种学习向量神经网络的图像插值算法
4) Learning vector quantization algorithm
学习向量量化算法
5) improved learning vector quantization
改进的学习向量量化
6) learning vector quantization
学习矢量量化
1.
Remote sensing image classification based on hybrid learning vector quantization algorithm;
基于混合学习矢量量化算法的遥感影像分类
2.
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.
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。
3.
The energies in different frequency bands selected as robust feature vectors, four types of forearm movement are identified through learning vector quantization neural network.
分析了特征提取方法并采用小波包变换各频段能量构造特征矢量,经过学习矢量量化神经网络训练能够有效地从伸肌和屈肌采集的两道肌电信号中识别伸拳,展拳,腕内旋,腕外旋4种运动模式,平均识别率为94。
补充资料：部分学习与整体学习
部分学习与整体学习
part learning and whole learning
部分学习与整体学习(part learningand whole learning)在运动学习和记忆学习中，根据对学习内容的处理方式可以分成部分学习和整体学习。部分学习就是将材料分成几个部分，每次学习一个部分:整体学习就是每次学习整个材料。一般来讲，整体学习的效果优于部分学习。但是，课题复杂彼此没有意义联系的材料，用部分学习的效果好:课题简短或具有意义联系的材料，用整体学习的效果好。在进行学习时，可以将部分学习与整体学习结合起来，先进行整体学习再进行部分学习，或者相反。这种相互结合的学习方式叫做综合学习，效果更好些。 (周国帕撰成立夫审)
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条