基于贝叶斯决策边界和空闲神经元修剪的神经分类器优化

M. Silvestre, L. Ling
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引用次数: 5

摘要

本文描述了一种基于贝叶斯决策边界和剪枝技术的模式分类特征提取算法。该方法能够通过保留隐藏层中真正有助于正确分类的神经元来优化MLP神经分类器。此外,我们还提出了一种基于训练样本的茎叶图来定义隐藏层神经元的合理数量的方法。实验验证了该方法的有效性。
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Optimization of neural classifiers based on Bayesian decision boundaries and idle neurons pruning
In this article we describe a feature extraction algorithm for pattern classification based on Bayesian decision boundaries and pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that really contribute to correct classification. Also, we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method.
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