本文对极大或极小数据集下的贝叶斯网络学习进行了研究，并提出了相关的解决方案。
This thesis is about the study on learning Bayesian Network from extremely large or small datasets and its application.
该方法可避免现有的贝叶斯网络学习过于依赖数据、对数据的数量和质量要求过高等问题。
This method can avoid the problems of depending on a large number of data with high quality in existing Bayesian network learning.
贝叶斯网络的学习。
动态贝叶斯网络(DBN)，以其扩展性和对时间序列的强大描述、推导和学习能力，逐渐被应用于连续语音识别中。
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
本文在分析了多种贝叶斯网络结构学习算法的基础上，并且根据水电仿真的应用背景，提出了一种根据多专家提供的规则库进行贝叶斯网络结构学习的新算法。
The Thesis analyses many kinds of Algorithm about Bayesian network structure learning, and then Setting-up a new Algorithm about structure learning Foundation on hydro-electrical simulation system.
在现实世界中，不完整数据是广泛存在的，如何从不完整数据中学习贝叶斯网络的参数和结构一个非常实用而有价值的问题。
In the real world, not exact data exist here and there, how to learn the parameters and structure of Bayesian Networks from data is of practical value greatly.
研究了贝叶斯网络的学习问题，包括贝叶斯网络结构学习和贝叶斯网络参数学习。
The learning of Bayesian Networks is studied, including structure learning of Bayesian Networks and parameter learning of Bayesian Networks.
在一个实施例中，机器学习算法生成贝叶斯网络。
In one embodiment, the machine learning algorithm generates a Bayesian network.
通过概念聚类识别孤立点，运用规划识别技术和贝叶斯因果网络实现目标的预测、识别，最终实现系统自学习。
The system applies conceptual clustering technology to recognize outliers, and uses plan recognition and causal network to predict and recognize the target.
贝叶斯网络的学习是数据挖掘中非常重要的一个环节，是将先验知识和模型评价融入训练数据，获得数据中隐藏的拓扑结构和参数的过程。
The learning of Bayesian Networks is an important tache, which combines training data with prior knowledge and model evaluation to acquire the structure hidden in data and parameters.
采用基于依存分析改进贝叶斯网络的无指导的机器学习方法对汉语大规模真实文本进行词义消歧实验。
The Word Sense Disambiguation (WSD) study based on large scale real world corpus is performed using an unsupervised learning algorithm based on DGA improved Bayesian Model.
其次，建立了一种小规模数据集下学习贝叶斯网络的有效算法FCLBN。
Secondly, an efficient algorithm FCLBN for learning Bayesian network from extremely small datasets is proposed.
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。
In the process of modeling BNs, the structure learning and parameter learning of BNs are analyzed detailedly.
实验结果表明IBN-M算法在数据缺失下贝叶斯网络的增量学习中确实能够学出相对精确的网络模型，该算法也是对贝叶斯网络增量学习方面的一个必要的补充。
The experiments show that IBN-M algorithm can learn comparatively accurate network from the extremely large dataset. IBN-M is an interesting improvement for incremental learning Bayesian Network.
对贝叶斯网络的参数学习进行了探讨，结合实例统计和相关性分析建立了车身偏差诊断的贝叶斯网络模型。
Parameter study of Bayesian network is investigated. According to the methods of example statistics and correlation analysis, Bayesian diagnosis model of body deviation is established.
在贝叶斯神经网络中，贝叶斯正则化技术被用来学习神经网络结构。
In the Bayesian neural network, Bayesian regularization technique has been used to study the structure of neural network.
在贝叶斯神经网络中，贝叶斯正则化技术被用来学习神经网络结构。
In the Bayesian neural network, Bayesian regularization technique has been used to study the structure of neural network.
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