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learning vector quantization中文是什么意思
发音:
- 中文翻译
- 造句
- 学习向量量化
- lvq learning vector quantization 学习向量量化
- hvq hierarchical vector quantization 分层矢量量化
- lvq lattice vector quantization 点阵矢量量化
- vq vector quantization 矢量量化，纹理压缩（加速芯片的功能）; 纹理压缩
- vqc vector quantization code 矢量量子化代码
- quantization 分层量子化把连续量转换为数字; 量化 在每个象素处，将图像的局部特性赋予一个灰度集合中的元素的过程。数字图象处理技术词汇表; 量化 在每一个像素处，将图像的局部特性赋予一个灰度集合中的元素的过程; 量化,分层,量子化; 量子化,量化(把连续量变为数字量),整量化; 量子化；数字化；分层; 量子化作用；分层; 取离散值
- adaptive quantization 自适应量化
- amplitude quantization 振幅量化
- binary quantization 二进制量化
- canonical quantization 二次量子化; 正则量子化
- charge quantization 电荷量子化
- conductance quantization 电导量子化
- differential quantization 差分量化
- digital quantization 数字量子化,数字量化
- dirac quantization 狄拉克量子化; 狄喇克量子化
- directional quantization 方向量子化
- field quantization 场量子化
- first quantization 一次量子化
- flux quantization 磁通量量子化
- geometric quantization 几何量子化
- imaging quantization 影像量化
- macroscopic quantization 宏观量子化
- measures of quantization 量化测量
- nonuniform quantization 非均匀量化
- phase quantization 相位量化
- 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。 - The main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed; the xor problem is implemented by using pnn . a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons . simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn
本文主要分析了pnn隐层神经元个数,隐中心矢量,平滑参数等要素对网络分类效果的影响,并用pnn实现了异或逻辑问题;提出了一种新的pnn有监督学习算法:用学习矢量量化对各类训练样本进行聚类,对平滑参数和距离各类模式中心最近的聚类点构造区域,并采用遗传算法在构造的区域内训练网络,实验表明:该算法在分类效果上优于其它pnn学习算法
- learning vector quantizationとは意味：学習｛がくしゅう｝ベクトル量子化｛りょうしか｝
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Last modified time:Sun, 10 Aug 2025 00:29:56 GMT
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