GSOM:成长自组织地图
“Growing Self-Organizing Map”通常被简称为GSOM,以方便快速书写和使用。这一术语广泛用于计算机科学领域,尤其是数据挖掘和模式识别方向,其中文含义为“成长自组织地图”,是一种基于自组织映射(SOM)的扩展模型,能够动态调整网络结构以适应输入数据的分布特征。
Growing Self-Organizing Map具体释义
Growing Self-Organizing Map的英文发音
例句
- Discovering rules using the growing self-organizing map neural networks and hierarchical clustering algorithm.
- 利用生长自组织映射神经网络,采用分级聚类SOM算法发现规则;
- Taking advantage of the feature of gray relation analysis which can extract the importance of each branch in sample vectors, an improved growing hierarchical self-organizing map algorithm is proposed.
- 利用灰关联分析可以体现样本向量中各分量重要性的特性,对生长、分级的自组织映射(growinghierarchicalself-organizingmap,GHSOM)网络进行了改进。
- Growing hierarchical self-organizing map model s for mental task classification
- 应用生长、分级的自组织映射模型进行意识任务分类
- The growing hierarchical self-organizing map ( GHSOM ) model was proposed to apply to performing mental tasks classification in EEG for Brain-Computer Interface.
- 提出一种使用生长、分级的自组织映射(growinghierarchicalself-organizingmap,GHSOM)模型进行基于EEG信号的意识任务分类来实现脑机接口技术的方法。
- Clustering Method of Time Series Based on Growing Hierarchical Self-organizing Map
- 基于GHSOM网络的时间序列聚类方法
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