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首页> 外文期刊>IEEE Transactions on Neural Networks >Representation and generalization properties of class-entropy networks
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Representation and generalization properties of class-entropy networks

机译：类熵网络的表示和推广性质

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摘要

Using conditional class entropy (CCE) as a cost function allows feedforward networks to fully exploit classification-relevant information. CCE-based networks arrange the data space into partitions, which are assigned unambiguous symbols and are labeled by class information. By this labeling mechanism the network can model the empirical data distribution at the local level. Region labeling evolves with the network-training process, which follows a plastic algorithm. The paper proves several theoretical properties about the performance of CCE-based networks, and considers both convergence during training and generalization ability at run-time. In addition, analytical criteria and practical procedures are proposed to enhance the generalization performance of the trained networks. Experiments on artificial and real-world domains confirm the accuracy of this class of networks and witness the validity of the described methods.
机译：使用条件类熵（CCE）作为成本函数，可以使前馈网络充分利用与分类相关的信息。基于CCE的网络将数据空间划分为多个分区，这些分区分配有明确的符号并由类别信息标记。通过这种标记机制，网络可以在本地级别对经验数据分布进行建模。区域标记随着网络训练过程的发展而变化，该过程遵循可塑性算法。本文证明了有关基于CCE的网络性能的几个理论特性，并考虑了训练期间的收敛性和运行时的泛化能力。另外，提出了分析标准和实践程序以增强训练网络的泛化性能。在人工和真实域上进行的实验证实了此类网络的准确性，并证明了所描述方法的有效性。

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