A multiple sensor information fusion algorithms-posterior probability detection algorithms is presented and applied to target identification.
提出了一种用于目标识别的多传感器雅息融合算法—后验概率检测算法。
Monte Carlo method can generate a large collection of models according to the posterior probability distribution and analyses and display the models with relative likelihood of model properties.
蒙特卡洛法能根据后验概率分布产生大量的模型，并能用模型的相关似然性质来分析和呈现这些模型。
Then the segmentation problem is formulated as Maximum a Posterior Probability (MAP) estimation rule.
分割问题可以被转换成一种最大后验概率估计问题。
In this paper, a posterior-probability-based feature selection algorithm is proposed for imbalanced datasets.
针对不平衡数据集，提出一种基于后验概率的特征选择算法。
A SVM model based on the posterior probability is bring forward by using a commercial bank's housing credit data.
利用某商业银行的住房信贷数据构建了基于后验概率的支持向量机评估模型。
A SVM model based on the posterior probability is bring forward by using a commercial bank's housing credit data.
利用某商业银行的住房信贷数据构建了基于后验概率的支持向量机评估模型。
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