BAYESIAN NEURAL NETWORKS
BAYESIAN NEURAL NETWORKS
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DOI：
10.1007/bf00200801
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发表时间：
1989-01-01
影响因子：
1.9
通讯作者：
KONONENKO, I
中科院分区：
文献类型：
--
作者：
KONONENKO, I
定义了一个使用基本赫布学习规则和贝叶斯组合函数的神经网络。类似于Hopfield神经网络，证明了异步更新贝叶斯神经网络的收敛性。贝叶斯神经网络在四个医学领域的性能进行了比较，与各种分类方法。贝叶斯神经网络使用比Hopfield神经网络更复杂的组合函数，并且更经济地使用可用信息。“朴素”贝叶斯分类器通常优于基本贝叶斯神经网络，因为网络中的迭代会产生太多错误。通过限制迭代次数和增加固定点的数量，网络的性能优于原生贝叶斯分类器。贝叶斯神经网络被设计为非常快速和增量地学习。
A neural network that uses the basic Hebbian learning rule and the Bayesian combination function is defined. Analogously to Hopfield''s neural network, the convergence for the Bayesian neural network that asynchronously updates its neurons'' states is proved. The performance of the Bayesian neural network in four medical domains is compared with various classification methods. The Bayesian neural network uses more sophisticated combination function than Hopfield''s neural network and uses more economically the available information. The "naive" Bayesian classifier typically outperforms the basic Bayesian neural network since iterations in network make too many mistakes. By restricting the number of iterations and increasing the number of fixed points the network performs better than the native Bayesian classifier. The Bayesian neural network is designed to learn very quickly and incrementally.