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   bayesian learning 在 计算机软件及计算机应用 分类中 的翻译结果: 查询用时:0.183秒
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bayesian learning
相关语句
  贝叶斯学习
    Video Image Segmentation Based on Bayesian Learning
    基于贝叶斯学习的视频图像分割(英文)
短句来源
    Elaborate process descriptions of evaluating offers, belief revision and proposing counteroffers are presented, in particular, we analyze the use of Bayesian learning and reinforcement learning in negotiation process, restructuring the traditional Q-learning into a dynamic Q-leaming algorithm by introducing current beliefs and recency exploration bonus.
    在该谈判模型的基础上引入学习机制,并分别对评估提议、更新信念、生成提议等谈判过具有学习机制的电子商务自动谈判研究 摘要程作了详细阐述,重点分析了贝叶斯学习和强化学习技术在自动谈判中的应用。 对传统的分学习进行扩充,设计了基于Agent的当前信念和最近探索盈余的动态分学习算法。
短句来源
    The histogram colors and co-occurrence vectors have been calculated. Bayesian learning has been used to obtain these probability distribution functions from the video image inputs. The experimental results indicate that the proposed approach is able to learn a complex background of which the illumination changes either gradually or suddenly.
    为了对光线变化的图像进行顺利侵害,提出了一种利用贝叶斯学习方法来进行视频图像分割的算法,即先在每个像素点处对不断变化的背景建模,同时计算每个像素点处的颜色直方图,再用这些直方图来表示该像素点处特征向量的概率分布,然后用贝叶斯学习方法来进行判断,以确定在光线缓慢或者突然变化的时候,每个像素点是属于前景还是属于背景。
短句来源
    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。
短句来源
  bayesian学习
    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
短句来源
  “bayesian learning”译为未确定词的双语例句
    Naive Bayes Classifier based on bayesian Learning theory and maximum a posteriori probablty hypotheses, which is welcomed as its simplicity.
    朴素贝叶斯分类技术是以贝叶斯定理、最大后验假设等理论为基础,其分类模型由于简单、易于实现而受到普遍青睐。
短句来源
    In accordance with the characteristics of the mass spectrum database,many KDD algorithms,such as the property-oriented inductive algorithm,Multi_AdaBoost algorithm based on Boosting Native Bayesian Learning as well as statistical ways are adopted during the data pre-treating process,and the results make it possible for us to have a better understanding of the mass spectrum database,so lay a good foundation for constructing knowledge base of intellectual resolution process of mass spectrum.
    针对谱图数据库的特性,在利用数据库知识发现(KDD)技术对谱图数据库进行数据预处理过程中,利用面向属性的归纳法对数据属性间的相关性进行分析,Multi_AdaBoost算法进行聚类分析和统计方法对Beynon表进行审核等方面进行了研究,这样,对质谱库有了更进一步的认识,为构建质谱智能解析系统的知识库和质谱解析的智能化打下了良好的基础。
短句来源
    A text classification method was developed using a weighted adjustment measure to improve the vector space model (VSM) and the naive Bayesian classifier (NBC). The EM algorithm was then used for non tutor Bayesian learning and a Chinese/English text classification system was developed.
    提出了一种利用权值调整思想对向量空间法 (VSM)和朴素 Bayes分类器 (NBC)进行改进的文本分类方法 ,并探讨了利用 EM算法进行无导师 Bayes分类的方法 ,设计和实现了一个中英文文本分类系统 CZW。
短句来源
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  bayesian learning
A system-based decision logic predicated on subjective and objective probabilities is developed incorporating the Bayesian learning process.
      
Besides allowing for exact Bayesian learning, these results permit us to formulate a new class of tractable latent variable models in which the likelihood of a data point is computed through an ensemble average over tree structures.
      
Tractable Bayesian learning of tree belief networks
      
This paper uses a Bayesian learning model to assess the respective influence of different risk measurements on mortality risk perceptions.
      
Also, the results suggest that the determinants of risk perception are consistent with the predictions of a Bayesian learning framework.
      
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In accordance with the characteristics of the mass spectrum database,many KDD algorithms,such as the property-oriented inductive algorithm,Multi_AdaBoost algorithm based on Boosting Native Bayesian Learning as well as statistical ways are adopted during the data pre-treating process,and the results make it possible for us to have a better understanding of the mass spectrum database,so lay a good foundation for constructing knowledge base of intellectual resolution process of mass spectrum.

针对谱图数据库的特性,在利用数据库知识发现(KDD)技术对谱图数据库进行数据预处理过程中,利用面向属性的归纳法对数据属性间的相关性进行分析,Multi_AdaBoost算法进行聚类分析和统计方法对Beynon表进行审核等方面进行了研究,这样,对质谱库有了更进一步的认识,为构建质谱智能解析系统的知识库和质谱解析的智能化打下了良好的基础。

In accordance with the characteristics of mass spectrum databases,many KDD algorithms,such as the new match and index algorithm of mass spectrum,and the Multi AdaBoost algorithm based on boosting native Bayesian learning as well as statistical ways, are adopted during data preprocessing,and the results make it possible for us to better understand mass spectrum databases.This lays a good foundation for constructing knowledge bases and intelligent resolution processes for mass spectrum.

本文针对谱图数据库的特性 ,在利用数据库知识发现 (KDD)技术对谱图数据库进行数据预处理过程中 ,对质谱匹配算法、MultiAdaBoost聚类分析算法和Beynon表审核等方面进行了研究 ,使我们对质谱库有了更进一步的认识 ,为构建质谱智能解析系统的知识库和质谱解析的智能化打下了良好的基础

Text classification is the key to text mining which is used extensively in traditional information searches, web information queries and web mining. A text classification method was developed using a weighted adjustment measure to improve the vector space model (VSM) and the naive Bayesian classifier (NBC). The EM algorithm was then used for non tutor Bayesian learning and a Chinese/English text classification system was developed. Three sets of test results show that the weighted adjustment measure...

Text classification is the key to text mining which is used extensively in traditional information searches, web information queries and web mining. A text classification method was developed using a weighted adjustment measure to improve the vector space model (VSM) and the naive Bayesian classifier (NBC). The EM algorithm was then used for non tutor Bayesian learning and a Chinese/English text classification system was developed. Three sets of test results show that the weighted adjustment measure using scoring functions can improve the precision of text classification models such as VSM and NBC with the effect increasing with increasing size of the training text set. The maximum NBC precision is 86%.

文本分类是文本挖掘的基础与核心 ,可广泛应用于传统的情报检索和 Web信息的检索与挖掘等。提出了一种利用权值调整思想对向量空间法 (VSM)和朴素 Bayes分类器 (NBC)进行改进的文本分类方法 ,并探讨了利用 EM算法进行无导师 Bayes分类的方法 ,设计和实现了一个中英文文本分类系统 CZW。 3组实验数据表明 ,用某些评估函数调节单词权值可有效提高 VSM和 NBC等文本分类模型的精度 ,并且训练文本规模越大 ,改进的效果越明显。 NBC的分类精度最高可达 86 %。

 
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