Constructive training of probabilistic neural networks
Constructive training of probabilistic neural networks
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DOI：
10.1016/s0925-2312(97)00063-5
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发表时间：
1998-03-01
期刊：
影响因子：
6
通讯作者：
Diamond, J
中科院分区：
文献类型：
--
作者：
Berthold, MR;Diamond, J
本文提出了一种易于使用的，建设性的训练算法的概率神经网络，一种特殊类型的径向基函数网络。与其他算法相比，不需要预先定义网络拓扑。该算法在必要时引入新的隐藏单元，并单独调整现有单元的形状，以最大限度地减少误分类的风险。这导致与经典PNN相比更小的网络，因此可以使用大数据集。使用八个分类基准从StatLog项目，新算法相比，其他国家的最先进的分类方法。结果表明，该算法生成的概率神经网络，实现了可比的分类性能，这些数据集。只需要手动调整两个相当不重要的参数，并且没有过度训练的危险-算法清楚地指示训练结束。此外，由于隐藏层中缺乏冗余神经元，因此生成的网络很小。(C)1998 Elsevier Science B. V.保留所有权利。
This paper presents an easy to use, constructive training algorithm for probabilistic neural networks, a special type of radial basis function networks. In contrast to other algorithms, predefinition of the network topology is not required. The proposed algorithm introduces new hidden units whenever necessary and adjusts the shape of already existing units individually to minimize the risk of misclassification. This leads to smaller networks compared to classical PNNs and therefore enables the use of large data sets. Using eight classification benchmarks from the StatLog project, the new algorithm is compared to other state of the art classification methods. It is demonstrated that the proposed algorithm generates probabilistic neural networks that achieve a comparable classification performance on these data sets. Only two rather uncritical parameters are required to be adjusted manually and there is no danger of overtraining - the algorithm clearly indicates the end of training. In addition, the networks generated are small due to the lack of redundant neurons in the hidden layer. (C) 1998 Elsevier Science B.V. All rights reserved.