贝叶斯判别

  • 网络Bayesian discrimination;Bayes discrimination;Bayesdiscrimination
贝叶斯判别贝叶斯判别
  1. 三代棉铃虫发生程度与气象条件关系的贝叶斯判别

    Bayes Discrimination on Relationship between Occurrence Degree of the 3 ~ ( rd ) Generation Bollworm and Meteorological Condition

  2. 提出了1种基于PCA(主成分分析)的贝叶斯判别器用于检测灰度面部图像。

    A method using Bayesian classifier is presented , which is based on PCA ( Principle Component Analysis ) for face detection in gray scale images .

  3. 文章基于平均策略,使用BP神经网络对贝叶斯判别、费歇尔线性判别和logistic回归判别财务危机的输出新变量进行加权平均再判别,并和单一方法判别的效果比较。

    The auther discriminates finance crisis of corporations with the outcomes of Discriminant analysis , Fisher discriminant , and Logistic regression by means of BP neural network based on average strategy , and ( compares ) their effects .

  4. 在研究的第二阶段,以40家ST公司与同期的40家非ST公司作为分析样本,分别采用Logistic回归、贝叶斯判别和BP神经网络模型方法,建立判别模型。

    In the second phase of research , use 40 ST companies and 40 non-ST companies at the same period as analyze samples to establish Logistic regression model , Bayesian discrimination model and BP Neural Network model .

  5. 基于肤色模型和贝叶斯判别的人脸检测

    Human face detection based on skin color model and BDF

  6. 基于贝叶斯判别分析的上市公司财务危机预警模型研究

    Forewarning Model of Listed Companies against Financial Crisis Based on Bayesian Discrimination

  7. 区域地貌中的聚类分析及贝叶斯判别方法

    Method of cluster analysis and Bayes distinguish on regional landforms

  8. 一类贝叶斯判别问题的渐近解

    The asymptotic solution of a class of Bayesian decision problems

  9. 鱼雷目标检测的贝叶斯判别融合方法研究

    Target Detection of Torpedo Based on Bayers Decision Fusion Method

  10. 一种采用混合高斯模型与贝叶斯判别的彩色图像人脸检测方法

    Face detection based on color image using mixture Gaussian model and Bayesian discrimination

  11. 论述了多元统计分析方法中的贝叶斯判别分析方法在安全评价中的应用。

    The paper expounds the application of Bayes discriminant analysis in safety evaluation .

  12. 基于贝叶斯判别的驾驶行为危险状态辨识

    Risk Identification for Driving Behaviors Based on Bayesian Discrimination

  13. 房地产估价的贝叶斯判别法

    Bayes Different Approach to Real Estate Appraisal

  14. 贝叶斯判别准则在确定地震活动性参数空间分布函数中的应用

    Application of Bayesian criterion in determination of spatial distribution function of seismicity parameters in seismic area

  15. 用贝叶斯判别法和回归分析逐级预报早稻产量

    Rade by grade forecast the yield of early season rice with Bayes discriminatory method and the regression analysis

  16. 该方法基于混合高斯模型与贝叶斯判别技术,分两步实现,即运动目标分割和人脸特征识别。

    Then , Bayesian discriminating classifier is used for making judgment of the possible face area and gets the face detection r.

  17. 该算法针对获取检测门限的困难和随意性,通过人工参与学习,依据最小代价贝叶斯判别准则,求出一个运动区域分割门限,并将这个门限用于后续图像中运动区域的检测。

    In this algorithm according to minimum cost Bayes rule we acquire a threshold of moving object segmentation through human-participant learning and apply this threshold to moving object detection in posterior images .

  18. 根据贝叶斯判别准则,确定了两类蝗区夏蝗大发生关键气象因子的最佳临界距平值;

    According to Bayesian rule , the best critical departure from normal value of the key meteorological factors for the outbreak of summer locust was determined by two kinds of ecological area .

  19. 贝叶斯判别分析结果表明:基于10折交叉验证建立的籽棉品级判别模型的识别率在75.00%~92.86%之间,模型的平均识别率达83.20%。

    Based on 10-fold cross-validation , Bayes-criterion discrimination results showed that grading models of field sampling cottons were developed with accuracy of 75.00 % to 92.86 % , with average of 83.20 % .

  20. 我们充分结合红外序列图像和光流场模型的优点,提出一种有监督学习的基于帧间图像差异显著性检验和最小代价贝叶斯判别准则的运动目标检测算法。

    Making full use of the power of combining infrared imaging with optical flow model , we propose a moving object pre-detection algorithm based on supervised learning , image pair difference significance test and minimum cost Bayes rule .

  21. 本文采用两种无损检测方法,即计算机视觉方法和敲击振动法对种蛋的孵化品质进行研究,建立了种蛋孵化成活性的贝叶斯判别模型,识别无精蛋和死胚蛋的准确率较高。

    Computer vision and acoustic impulse response technique were used to study the fertility of hatching eggs , Bayesian discriminant models which proven to have high detection accuracy were built to distinguish infertile eggs and dead embryos . 1 .

  22. 针对知识水平,采用了贝叶斯判别的方法进行测量,避免了大规模测验;而认知风格的识别则采用了贝叶斯网络的方法,在现有认知风格分类的基础上实现了多重风格的概率表示。

    The Bayesian discriminant method was used to measure the knowledge level , which avoid the large-scale test . And the Bayesian network is adopted to recognize cognitive style , it realized the probability expression of multiple style based on the existing classification of cognitive style .

  23. 通过主成分分析、聚类分析、贝叶斯判别分析、典型判别分析等方法分别对不同树种组成的森林植被类型水源涵养功能等级进行判别,并对功能等级属于第Ⅳ类的低功能林分列出划分技术参数。

    ( 5 ) Using principal component analysis , cluster analysis , bayes discriminant analysis , typical discriminatory analysis , to differentiate different species composition of the water conservation function of forest vegetation types , and functional category of low-function stand ⅳ into technical parameters are listed .

  24. 一种利用贝叶斯最小判别准则估计未来地震危险的新方法

    A New Method on Estimation of Future Seismic Risk Based on Bayesian Minimun Criterion

  25. 研究了人脸检测的贝叶斯特征判别法,该方法包括三个部分:原始图像的特征判别分析、人脸区和其它区的统计建模以及贝叶斯分类器。

    The main idea of which consists of three parts : the discriminating feature analysis of the images , the statistical modeling of face and non-face classes , and the Bayes classifier for face detection .

  26. 同时,算法中加入的贝叶斯信息判别准则能够选择出最佳的聚类模型,从而达到自动划分聚类数的目的,尽可能的减少了识别过程中人为因素的影响。

    Meanwhile , adding Bayesian Information Criterion to the algorithm , the method could chose the optimum clustering model and thus achieved automatic dividing clusters . Furthermore , it reduced the influence of the human factors in the identification process .

  27. 贝叶斯(Bayes)判别分析理论在安全评价中的应用

    Application of Bayes Discriminant Analysis in Safety Evaluation

  28. 贝叶斯(Bayes)判别分析在粉煤灰分类中的应用关于Bayes风险相容性

    The Application of Bayes Discriminant Analysis in the Classification of Fly Ash ON THE CONSISTENCY OF BAYES RISKS

  29. 本文直接利用原始测井曲线,应用贝叶斯(Bayes)判别分析方法确定出了气水界面。

    In this paper , gas-water contact is determined directly with raw data by means of Bayes discriminant analysis .

  30. 该方法同样是以EM算法实现的极大似然估计方法实现各个类参数估计,以个体所属类别的贝叶斯后验概率判别个体的归类。

    In this method the parameters of different clusters were also estimated by the ML method implemented via EM algorithm and the individuals were classified by the Bayesian posterior probabilities .