The new algorithm is based in the property of tendency and normal distribution of consistent Bayesian learning.
新算法是以相容的贝叶斯学习的渐进正态性为理论基础。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.
我们将稀疏贝叶斯学习与序列蒙特卡罗盲均衡算法结合，提高了原算法的性能。
Bayesian learning is a probability method that makes optimal decision based on known probability distribution and recently observed data.
贝叶斯学习是一种基于已知的概率分布和观察到的数据进行推理，做出最优决策的概率手段。
Then, giving the sample words from library, a categorization database is created and used for automatic categorization of Chinese journals by Bayesian learning.
其次，通过对图书馆的样本数据进行训练建立的分类库，本文使用贝叶斯分类器实现中文期刊的自动分类。
Based on a rank-1 update, we propose sparse Bayesian Learning Algorithm (SBLA), which has low complexity and high sparseness, thus being very suitable for large-scale problems.
基于秩- 1更新，提出了稀疏贝叶斯学习算法(SBLA)。该算法具有较低的计算复杂度和较高的稀疏性，从而适合于求解大规模问题。
This method can avoid the problems of depending on a large number of data with high quality in existing Bayesian network learning.
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。
The learning of Bayesian Networks.
贝叶斯网络的学习。
Then in allusion to these two important factors, a concept of incremental learning and a loss extent parameter are put forward in this paper, and Native Bayesian Classification.
文中针对该算法这两个最主要的缺陷，提出增量学习概念，引入损失幅度参数，改进和完善朴素贝叶斯分类算法。
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
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。
This thesis is about the study on learning Bayesian Network from extremely large or small datasets and its application.
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。
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.
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 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.
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。
In one embodiment, the machine learning algorithm generates a Bayesian network.
在一个实施例中，机器学习算法生成贝叶斯网络。
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.
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。
First, we summarize some amS learning algorithms on Gaussian or finite mixture based on the Bayesian Ying-Yang (BYY) harmony learning principle.
首先，我们综述了基于贝叶斯阴阳机和谐学习原则的自动模型选择学习算法。
Secondly, an efficient algorithm FCLBN for learning Bayesian network from extremely small datasets is proposed.
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。
The new algorithm combines the merit of decision tree induction method and naive Bayesian method. It retains the good interpretability of decision tree and has good incremental learning ability.
该算法综合了决策树方法和贝叶斯方法的优点，既有良好的可解释性，又有良好的增量学习能力。
The learning of Bayesian Networks is studied, including structure learning of Bayesian Networks and parameter learning of Bayesian Networks.
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 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.
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.
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。
应用推荐