Batch-Learning Self-Organizing Map with Weighted Connections avoiding false-neighbor effects
Batch-Learning Self-Organizing Map with Weighted Connections avoiding false-neighbor effects
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DOI：
10.1109/ijcnn.2010.5596524
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发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
H. Matsushita;Y. Nishio
中科院分区：
文献类型：
--
作者：
H. Matsushita;Y. Nishio
提出了一种加权连接避免伪邻效应的批处理学习自组织映射算法(BL-WCSOM)。我们将BL-WCSOM应用于几个高维数据集。从量化误差、非活跃神经元、地形误差和计算时间的测量结果可以看出，该算法以较少的神经元在较短的时间内得到了反映输入数据分布状态的有效地图。
This study proposes a Batch-Learning Self-Organizing Map with Weighted Connections avoiding false-neighbor effects (BL-WCSOM). We apply BL-WCSOM to several high-dimensional datasets. From results measured in terms of the quantization error, inactive neurons, the topographic error and the computation time, we confirm that BL-WCSOM obtain the effective map reflecting the distribution state of the input data using fewer neurons in less time.