This paper presents a training algorithm for probabilistic neural networks using the MCE criterion.
提出了一种基于最小分类错误准则的概率神经网络的训练算法。
When applied to the Probabilistic Neural Networks, the approach improves its two inherent shortcomings.
当应用在概率神经网络分类时，可对其固有的两个缺点都有所改善。
This paper presents an efficient training algorithm for probabilistic neural networks using the minimum classification error criterion.
提出了一种基于最小分类错误准则的概率神经网络的训练算法。
A novel graphic symbol recognition approach of engineering drawings based on radial basis probabilistic neural networks (RBPNN) is proposed.
基于径向基概率神经网络，提出一种扫描工程图纸图像分割后的图形符号识别方法。
Probabilistic Neural Networks (PNN) is improved and used on line to estimate the states of singular perturbed systems, especially to the fast states of the systems.
将改进的概率神经网络(PNN)用于奇异摄动系统的实时状态估计，注重针对系统快变部分的滤波。
The result indicates that probabilistic neural networks can localize the single damage correctly, and the networks with the compounded index show better effectiveness.
发现基于概率神经网络的结构损伤定位方法能够正确识别单一位置损伤，且组合参数作为输入指标时的识别效果更好。
Furthermore, on the basis of feature extraction, by utilizing the Radial basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically.
在特征提取的基础上，进一步利用径向基概率神经网络(RBPNN)分类器，实现了掌纹的自动识别。
Their success sparked a renewed interest in learning AI methods such as decision trees, neural networks, genetic algorithms, and probabilistic methods.
这些游戏的成功从新燃起了对游戏ai方法的热情，比如：决定树，神经元网络，遗传算法，和盖然论。
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.
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.
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。
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