| 1. | Design of an improved spam filter based on naive bayesian classifier
垃圾邮件过滤器的改进 |
| 2. | Face detection with bayesian classifier
基于贝叶斯判别器的面部检测 |
| 3. | 3 ) we construct the privacy preserving naive bayesian classifier
3 )构造了保持隐私的朴素贝叶斯分类器。 |
| 4. | The key of model learning of semi - naive bayesian classifier is how to combine feature attributes effectively
目前半朴素贝叶斯分类模型学习的关键是如何有效组合特征属性。 |
| 5. | This thesis makes a study of two bayesian classifying models which are semi - naive bayesian classifier and increasing bayesian classifier
本文从两个方面对贝叶斯分类模型进行了深入的研究：半朴素贝叶斯分类与增量贝叶斯分类。 |
| 6. | The oblivious polynomial evaluation protocol will be used many times in our privacy preserving naive bayesian classifier , so its efficiency is important to the solution
健忘多项式计算协议在保持隐私的朴素贝叶斯分类器协议中多次用到，因此协议的效率是一个需要关心的问题。 |
| 7. | The key of increasing bayesian classifier is the policy of how to choose test samples . this thesis studies how to make full use of prior knowledge and transmit it
增量贝叶斯分类模型的关键是测试实例的选择策略，本文研究的重点是如何充分利用训练集的先验知识并使其在学习过程中向前传递，提出了新的模型。 |
| 8. | Theoretical analyses and experimental results demonstrate that this method is very effective . also , bayesian classifier , subspace method and ann are summarized in this chapter . they can be used for the next research
本章还对贝叶斯分类器，子空间模式识别和人工神经网络在字符识别中的应用进行了总结，可作为进一步研究的基础。 |
| 9. | By constructing two secure posterior probability evaluation protocols to deal with discrete and numeric , or categorical and continuous attributes respectively , we attain the naive bayesian classifier without preamble
本文针对离散值属性情形和连续值属性情形分别构造了保持隐私的后验概率计算协议，最后获得安全的朴素贝叶斯分类器协议。 |
| 10. | Since most algorithms are not effective and not very meaningful in combining , this thesis proposes an algorithm based on a kind of semi - naive bayesian classifier which is measured by conditional mutual information ( cmi - bsnbc )
针对已有的学习算法中存在的效率不高及部分组合意义不大的问题，本文提出了条件互信息度量半朴素贝叶斯分类学习算法( cmi - bsnbc ) 。 |
