A neural network based on LVQ2 with dynamic building of the map

E. Maillard, B. Solaiman
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引用次数: 3

Abstract

HLVQ network achieves a synthesis of supervised and unsupervised learning. Promising results have been reported elsewhere. A dynamic map-building technique for HLVQ is introduced, During learning, the creation of neurons follows a loose KD-tree algorithm. A criterion for the detection of the network weakness to match the topology of the training set is presented. This information is localized in the input space. When the weakness criterion is matched, a neuron is added to the existing map in a way that preserves the topology of the network. This new algorithm sets the network almost free of a crucial external parameter: the size of the neuron map. Furthermore, it is shown that the network presents highest classification score when employing constant learning rate and neighborhood size.<>
基于LVQ2的地图动态构建神经网络
HLVQ网络实现了监督学习和无监督学习的综合。其他地方也报道了令人鼓舞的结果。在学习过程中,神经元的创建遵循一个松散的kd树算法。提出了一种检测网络弱点以匹配训练集拓扑的准则。该信息在输入空间中被定位。当弱点标准匹配时,以保持网络拓扑结构的方式在现有映射中添加一个神经元。这种新算法使网络几乎不受一个关键外部参数的影响:神经元图的大小。进一步研究表明,当采用恒定的学习率和邻域大小时,网络的分类分数最高。
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