中文摘要：

针对连续属性朴素贝叶斯分类器不能有效利用属性之间的条件依赖信息，而依赖扩展又很难实现属性条件联合密度估计和结构学习协同优化的问题，文中在使用多元高斯核函数估计属性条件联合密度的基础上，建立了具有多平滑参数的连续属性完全贝叶斯分类器，并给出将分类准确性标准与区间异步长划分完全搜索相结合的平滑参数优化方法，再通过时序扩展构建了动态完全贝叶斯分类器．我们使用UCI机器学习数据仓库中连续属性分类数据和宏观经济数据进行实验，结果显示，经过优化的两种分类器均具有良好的分类准确性．

英文摘要：

The naive Bayes classifiers with continuous attributes can not make the effective use of conditional dependency information between attributes. In dependency extension of naive Bayes classifiers, it is very difficult that the optimization of attribute conditional joint density estimation and structure learning of classifiers are integrated. In this paper, on the basis of using multivariate Gaussian kernel function to estimate the conditional joint density of attributes, a full Bayes classifier with continuous attributes and multi smoothing parameters is presented. The smoothing parameters are optimized by combining the evaluation criteria of classification accuracy and full search method based on interval division with asynchronous long. A dynamic full Bayes classifier is also developed by combining full Bayes classifier with time series. Experiment and analysis are done by using data sets with continuous attributes in UCI machine learning repository and macroeconomic field. The results show that two kinds of optimized classifiers have very good classification accuracy.