The stability of object types is one bottleneck in BP, leading to its robustness less than naive Bayesian network.
地物类别的稳定性是BP识别算法效率的瓶颈，导致其健壮性不如朴素贝叶斯网络。
Therefore, the authors propose a new approach of Augmented Naive Bayesian network based on Association Rules data mining to diagnose faults in power network.
针对以上问题，本文研究采用关联规则属性约简和贝叶斯网络相结合的电网故障诊断方法。
It is mainly of two kinds, Naive Bayesian Classification and Bayesian Belief Network Classification.
它主要有两种分类方法：一种为朴素贝叶斯分类，另一种为贝叶斯信念网络分类。
It is mainly of two kinds, Naive Bayesian Classification and Bayesian Belief Network Classification.
它主要有两种分类方法：一种为朴素贝叶斯分类，另一种为贝叶斯信念网络分类。
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