基于贝叶斯方法的分类预测

The Classification and Prediction Based on Bayes Theorem

介绍了基本的贝叶斯分类模型和贝叶斯信念网络模型,对网络模型的学习进行了讨论。并从实际出发,提出了几种可以简化模型结构、降低学习复杂性的可行方法,简要说明了这些方法在网络模型中的应用。对贝叶斯分类模型的准确性及其主要特点进行了分析。

The nakve Bayesian classification model and the Bayesian belief networks model are introduced.The learning of a belief network is discussed.Some feasible methods that can simplify the model structure and reduce the learning complexity are proposed,and the application of these methods to the belief network is explained briefly.The accuracy and the main features of Bayesian classification models are analyzed.