Bayesian learning

Research on Bayesian Learning Theory and Its Application

贝叶斯学习理论及其应用研究

Relevance Vector Machine Based on Bayesian Learning and Its Application in Soft Sensing

基于贝叶斯学习的关联向量机及其在软测量中的应用

Chapter four is " Static and Dynamic Bayesian Learning " .

第四章是探讨静态信息和动态信息的贝叶斯学习。

Research of Multi-Agent Negotiation Mechanism Based on Bayesian Learning

基于Bayesian学习的多Agent谈判机制研究

A Fuzzy Bayesian Learning Model in Agent-based Electric Power Bilateral Negotiation

多智能体代理下电力双边谈判中的模糊贝叶斯学习模型

Approach of Image Reconstruction Based on Sparse Bayesian Learning

基于稀疏贝叶斯学习的图像重建方法

An Improved Bayesian Learning Scheme for Interactive Harmful Information Filtering

一种用于互动型不良信息过滤的贝叶斯改进方案

A semantic description model of lung cancer chromatic images based on Bayesian learning

一种基于Bayesian学习的彩色肺癌图像语义描述模型

Enhancing relevance feedback in image retrieval using asymmetric Bayesian learning

基于不对称贝叶斯学习的图像检索相关反馈算法研究

Bayesian learning and inference algorithm based on Bayesian Networks Toolbox

基于贝叶斯网络工具箱的贝叶斯学习和推理

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。

The representative algorithms include Boosting , SVM and Bayesian learning .

代表算法包括Boosting算法，SVM和贝叶斯学习等。

Studying on Prior Distribution in Bayesian Learning

贝叶斯学习的先验分布的研究

A Bayesian Learning Algorithm Based on Search-Coding Method

基于搜索编码的简单贝叶斯分类方法

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。

This paper firstly researches these popular machine learning , such as Bayesian learning , reinforcement learning and genetic algorithm .

论文分别对贝叶斯学习、增强学习、遗传算法等几种主流的学习机制进行研究。

Bayesian learning is used in conjunction with RMM for belief update .

对RMM中的信念更新采用贝叶斯学习方法，使智能体可以确定其它智能体的准确模型并实时更新信息。

In the algorithm ; Boosting Naive Bayesian Learning

算法简单易行。增强型朴素贝叶斯学习

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。

The sparse Bayesian learning algorithm guarantees the sparsity of the weighting coefficient matrix .

稀疏贝叶斯学习算法的应用，使得加权系数矩阵具有很好的稀疏性，保证了重建图像的质量。

Boosting Naive Bayesian Learning

增强型朴素贝叶斯学习

Discussing the necessary of learning , and analyzing the application of Bayesian learning and dynamic Q-learning algorithm to formalized multi-issue negotiation model .

论述了学习的必要性，并将贝叶斯学习和动态Q-学习引入前一章所提出的谈判模型中。

The model based on expert agents combines the virtue of Bayesian learning and Non-Bayesian learning , which has good swiftness and convergence .

基于专家节点的社会学习模型结合了贝叶斯学习和非贝叶斯学习的优点，即具有快速性，同时有好的收敛性。

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。

A modified Bayesian learning method for modular neural network is proposed in Chapter 3 . In Chapter 4 we present another combination method for modular neural network-sequential Bayesian method .

第3章提出一种改进的模块化神经网络贝叶斯学习法。第4章基于贝叶斯决策的序贯分析思想提出一种用于模块化神经网络的序贯贝叶斯学习法。

Combining sparse Bayesian learning with the principle of the support vector tracking ( SVT ), the relevance vector tracking ( RVT ) is presented .

结合稀疏贝叶斯学习方法和支持向量跟踪（SVT）原理，提出了相关向量跟踪（RVT）。

In this paper , the sequential game theory was used to design the trading mechanism between the two sides , and the Bayesian learning model was introduced to enhance the two sides'learning ability .

采用序贯博弈理论设计二者的交易机制，引入贝叶斯学习模型以提高发电商和用户的学习能力。

In this thesis , the basic philosophy , current research and significance of Bayesian Learning Theory are discussed . It also investigates the representation , learning and inference mechanism of Bayesian network .

本文介绍了贝叶斯学习理论的基本观点和它的研究现状与意义，并就贝叶斯网络的表达能力、学习过程和推理机制进行了研究和讨论。

The temporal characteristics of source signals are exploited and some appropriate models are adopted to describe the temporal structure . All our separation algorithms are based on variational Bayesian learning .

本文所提出的盲分离算法都是建立在变分贝叶斯学习的基础上，充分利用了每个源信号本身的时间相关特性，采用了合适的模型描述其时间结构。

In this paper , we discuss the basic concepts of TAN classifier and the algorithm based on Bayesian learning . Join the classifier algorithm and the concrete classification algorithm into an effective algorithm .

文中讨论了基于贝叶斯学习的TAN分类器的基本概念和分类算法，同时将分类器算法和具体分类算法结合为一个完整的有效算法。