新算法是以相容的贝叶斯学习的渐进正态性为理论基础。
The new algorithm is based in the property of tendency and normal distribution of consistent Bayesian learning.
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。
We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.
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。
Bayesian learning is a probability method that makes optimal decision based on known probability distribution and recently observed data.
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。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
在该协商模型的基础上引入贝叶斯学习机制，并分别对更新信念、生成提议等协商过程作了详细阐述。
We embed Bayes learning mechanism on the basis of the negotiation model, and elaborate process descriptions of evaluating offers, belief revision and proposing counter-offers are presented.
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。
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.
贝叶斯网络的学习。
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.
贝叶斯方法的特点是使用概率去表示所有形式的不确定性，学习或其他形式的推理都用概率规则来实现。
The characteristic of the Bayes method is to use probability to express the uncertainty of all forms, learning and the reasoning of other forms are all realized with the rule of probability.
一个简单的机器学习算法，朴素贝叶斯算法可以把正规邮件从垃圾邮件里面分离出来。
A simple machine learning algorithm called naive Bayes can separate legitimate email from spam email.
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.
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.
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.
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.
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.
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.
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.
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。
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.
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。
First, we summarize some amS learning algorithms on Gaussian or finite mixture based on the Bayesian Ying-Yang (BYY) harmony learning principle.
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.
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.
推导得到两种迭代学习辨识算法：迭代学习贝叶斯法和迭代学习随机牛顿法。
Two prototype algorithms of iterative learning identification, iterative learning Bayes and stochastic Newton algorithms, are proposed with detail.
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.
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.
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.
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