介绍了基于支持向量机的概率密度估计。
A new method for density estimation was developed based on the Support Vector Machine (SVM).
针对传统的支持向量机方法不能提供后验概率的输出问题，从信息熵的角度采用最大熵估计方法，直接对支持向量机输出进行后验概率建模。
To the problem that the standard SVM does not provide probabilities output, the probabilistic outputs for support vector machines is modeled based on the maximum entropy estimation.
支持向量机的分类误差与概率神经网络相近，但支持向量机的使用较概率神经网络简单。
The generalization error of Support Vector Machine is approximately equal to that of Probabilistic Neural Network. And Support Vector Machine is easier to use than Neural Networks.
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。
A SVM model based on the posterior probability is bring forward by using a commercial bank's housing credit data.
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