Bayesian Network learning mainly includes structure learning and parameter learning.
贝叶斯网络学习包括网络的结构学习和参数学习。
参考来源 - 基于量子遗传算法的贝叶斯网络结构学习A two stage algorithm for the parameters learning of the mixtures of factor analyzers is presented, which first approximates the probability distribution of the data by the Gaussian mixture models, and then performs factor analysis for each Gaussians.
给出了一种用于混合因子分析模型参数学习的两阶段学习算法,即首先使用混合高斯模型学习数据的概率分布,然后对每一个高斯混合项进行因子分析。
参考来源 - Bayes网络模型及其学习算法研究In the process of modeling BNs, the structure learning and parameter learning of BNs are analyzed detailedly.
详细分析了贝叶斯网络的建模过程,即贝叶斯网络的结构学习过程和贝叶斯网络的参数学习过程。
参考来源 - 软件项目风险管理理论与模型研究Dynamic fuzzy neural network (D-FNN), through its own parameters learning, changeable topological structure and etc, is able to guarantee the demands for performances under the severe situations arising from the uncertainties inside linear motor and outside disturbances.
动态模糊神经网络控制通过自身所具有的参数学习以及拓扑结构可变等功能,保证在直线电机内部不确定性和外部扰动对系统严重影响的情况下,也能满足性能要求。
参考来源 - 永磁直线同步电机动态模糊神经网络速度控制器设计·2,447,543篇论文数据,部分数据来源于NoteExpress
分析了动态递归神经网络系统辨识的参数学习算法。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed. D.
分离系统的线性部分和非线性部分参数学习都采用自然梯度算法。
The natural gradient method is applied for parameter learning of the linear and nonlinear parts of the separating system.
最后讨论了离散尺度与小波核函数的构造,核函数选择与核参数学习。
Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.
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