An effective Bayesian neural network classifier with a comparison study to support vector machine
An effective Bayesian neural network classifier with a comparison study to support vector machine
复制标题
DOI：
10.1162/08997660360675107
复制
发表时间：
2003-08-01
影响因子：
2.9
通讯作者：
Liang, FM
中科院分区：
文献类型：
--
作者：
Liang, FM
提出了一种新的贝叶斯神经网络分类器，该分类器在似然函数、先验指标和网络结构等方面与常用的贝叶斯神经网络分类器有所不同。在正则性条件下，我们证明了新的分类器所确定的决策边界将收敛到真实边界。我们还提出了一种新的分类器的系统实现。在我们的实现中，通过从网络结构和连接权的联合后验分布中进行采样，将连接权值的调整、隐含单元的选择和输入变量的选择统一起来。数值结果表明，新的分类器在泛化性能上一致优于常用的贝叶斯神经网络分类器和支持向量机。详细分析了常用的贝叶斯神经网络分类器和支持向量机分类效果不佳的原因。
We propose a new Bayesian neural network classifier, different from that commonly used in several respects, including the likelihood function, prior specification, and network structure. Under regularity conditions, we show that the decision boundary determined by the new classifier will converge to the true one. We also propose a systematic implementation for the new classifier. In our implementation, the tune of connection weights, the selection of hidden units, and the selection of input variables are unified by sampling from the joint posterior distribution of the network structure and connection weights. The numerical results show that the new classifier consistently outperforms the commonly used Bayesian neural network classifier and the support vector machine in terms of generalization performance. The reason for the inferiority of the commonly used Bayesian neural network classifier and the support vector machine is discussed at length.