| 1. | Algorithm of image segmentation based on wavelet transform and bayesian theory
基于小波变换和贝叶斯理论的图像分割算法 |
| 2. | Bayesian theory and bayesian models are system introduced in the thesis
本文对贝叶斯理论和贝叶斯模型进行介绍和讨论。 |
| 3. | Research on risk decision making for real estate investment based on bayes decision theory
基于贝叶斯理论的房地产投资风险决策研究 |
| 4. | A new approach is developed to estimate the mixed ? eibull distribution ? s parameters using the bayes " theory
利用贝叶斯理论给出了估计混合威布尔分布参数的一种新方法。 |
| 5. | This thesis discusses how to acquire knowledge in incomplete information system by bayes theory , d - s evidence theory , analyse the relation and difference of them , and illuminate it by example
本文探讨了证据理论、贝叶斯理论在不完备信息系统中如何进行规则提取，分析了它们两者之间的联系与区别，并通过实例加以说明。 |
| 6. | This technique is build on bayesian theory . combining probability statistical learning and scene estimating , it can generate a complexing edge detection pattern . we call this technique edbml
该技术以贝叶斯理论为基础，应用概率统计学习与景物估计相结合的方式为原模糊图像生成了一个合成的边缘标记结果图，把这个技术称作edbml 。 |
| 7. | To limit the predicting precision loss in a certain range , author presented a method of bayes modeling and predicting for dynamic errors based on standard value interpolation at intervals during the multi - step prediction after consulting a lot of papers at home and abroad
为将预报精度损失控制在一定的范围之内，作者在查阅了国内外大量相关文献之后，提出了基于标准量插入的动态测量误差的贝叶斯建模预报理论，并根据贝叶斯理论给出了预报值的不确定度。 |
| 8. | This article has analyised the bayesian theory and proposed a way of improving its filtering technique against chinese mails . after pre - handling the mails . it will deal with them by phrases and then compress the characteristic dimension of the mail collection by using the reduction method of the best attribute of the dependent rough set
对朴素贝叶斯理论作为中文邮件过滤技术进行了分析改进,邮件预处理后,对其进行分词处理,利用基于依赖性的粗糙集最优属性约简方法来对邮件集进行特征维数压缩。 |
| 9. | In this paper , according to the characteristic and essence of data mining and bayes method , bayes theory are applied to the methods of data mining such as clustering , classification , association rule and abnormity analysis . some algorithms are brought forward and verified and discussed
本文主要针对数据挖掘的特点和本质，充分利用贝叶斯方法的特点，将贝叶斯理论及其思想方法融入到聚类、分类、关联规则挖掘以及偏差分析和异常检测等数据挖掘各方法中，提出将贝叶斯方法应用于数据挖掘的算法，并对提出的算法进行了验证和讨论。 |
| 10. | By combining bayesian principles and other priori knowledge , the method has improved the degree of accuracy of classification and overcome shortcomings of immense data quantity , complexity of calculation and slow speed of recognition which exist in traditional maximum likelihood classification in recognizing pictures
该方法通过贝叶斯理论与其他先验知识进行融合，提高了分类准确度，克服了传统最大似然分类法在图像识别过程中具有的数据量庞大、计算程度繁冗和识别速度慢等缺点。 |
