bayesian classification中文是什么意思

• 贝叶斯分类器

• "classification"中文翻译    n. 1.选别；分等，分级；分选。 2.【动、植】分类( ...
• "bayesian" 中文翻译 :    贝叶斯的; 贝叶斯定理的
• "bayes bayesian" 中文翻译 :    贝叶斯定理的
• "bayesian algorithm" 中文翻译 :    贝叶斯算法
• "bayesian analysis" 中文翻译 :    贝叶斯分析
• "bayesian approach" 中文翻译 :    bayes氏方法; 贝斯分析法
• "bayesian classifier" 中文翻译 :    贝叶斯分类器
• "bayesian estimate" 中文翻译 :    贝叶斯估计
• "bayesian estimation" 中文翻译 :    贝叶斯估计
• "bayesian formula" 中文翻译 :    式方程式
• "bayesian inference" 中文翻译 :    贝叶斯定理; 贝叶斯推断; 贝叶斯推理; 贝叶斯推论
• "bayesian learning" 中文翻译 :    贝式学习
• "bayesian logic" 中文翻译 :    贝叶斯逻辑
• "bayesian models" 中文翻译 :    贝式模型法
• "bayesian network" 中文翻译 :    贝式网路; 贝式网络; 贝叶斯网络
• "bayesian networks" 中文翻译 :    贝叶斯网络
• "bayesian probability" 中文翻译 :    贝叶斯概率
• "bayesian regression" 中文翻译 :    贝叶斯回归
• "bayesian statistic" 中文翻译 :    贝叶斯统计
• "bayesian statistics" 中文翻译 :    贝式统计学; 贝叶斯统计:一种尚有某些争论的统计方法学
• "bayesian theorem" 中文翻译 :    贝叶斯定理
• "bayesian theory" 中文翻译 :    贝叶斯理论
• "classification" 中文翻译 :    n. 1.选别；分等，分级；分选。 2.【动、植】分类(法)。 〔分类级别为： phylum 【动物；动物学】及 division 【植物；植物学】门，class 纲，order 目，family 科，genus 属，species 种，variety 品种〕。 3.类别；等级；(文件的)保密级。 a classification yard (车站的)调车场。
• "bayesian decision method" 中文翻译 :    贝叶斯决策方法
• "bayesian decision model" 中文翻译 :    培欣决策模式

• Bayesian classification model based on attribute correlation analysis
  基于属性相关性分析的贝叶斯分类模型
• According to the criteria , the advancement of bayesian classification is evident
  综合这几个指标，贝叶斯分类算法的优点较为突出。
• The bayesian classification and identification method based on normal - inverted wishart prior distribution
  先验分布的贝叶斯分类识别方法研究
• The often - used classification is classification by decision tree induction , bayesian classification and bayesian belief networks , k - nearest neighbor classifiers , rough set theory and fuzzy set approaches
  分类算法常见的有判定树归纳分类、贝叶斯分类和贝叶斯网络、 k -最临近分类、粗糙集方法以及模糊集方法。
• There are many techniques for data classification such as decision tree induction , bayesian classification and bayesian belief networks , association - based classification , genetic algorithms , rough sets , and k - nearest neighbor classifiers
  挖掘分类模式的方法有多种，如决策树方法、贝叶斯网络、遗传算法、基于关联的分类方法、粗糙集和k -最临近方法等等。
• Naive bayesian classification algorithm is not satisfying when deployed to continuous attribute . therefore , the paper proposes a new discretization method under the hint of holte ' s 1r ( one rule ) discretization technique and the mechanism of entropy
  朴素贝叶斯分类算法应用于连续属性值时并不太理想，为此本文结合holte的1r离散化方法和熵的原理，提出了一种新的离散化方法。
• Bayesian classification is based on bayesian theorem . it can be comparable in interpretability with decision tree and in speed with neural network classifiers . bayesian classifiers have also exhibited high accuracy and speed when applied to large databases
  该算法基于贝叶斯定理，可解释性方面可以与判定树相比，准确度可和神经网络分类算法相媲美，用于大型数据库时该算法已表现出高准确度与高速度。
• Unlike other classifications , bayesian classification bases on mathematics and statistics , and its foundation is bayesian theory , which answers the posterior probability . theoretically speaking , it would be the best solution when its limitation is satisfied
  与其它分类方法不同，贝叶斯分类建立在坚实的数理统计知识基础之上，基于求解后验概率的贝叶斯定理，理论上讲它在满足其限定条件下是最优的。
• After dividing proper nouns in two categories , this paper discusses different algorithms for these two categories : for the first category we use proper nouns database to recognize it , and for the second category we use the recognizing method base on native bayesian classification algorithm
  然后对这两类专有名词设计不同的识别方法:对第一类专有名词使用的基于专有名词词库的识别算法;对第二类专有名词使用的基于朴素贝叶斯分类的识别算法。
• Monte carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory . when comes to bayesian classification , it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability . then , it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one
  蒙特卡罗是一种采用统计抽样理论近似求解数学或物理问题的方法，它在用于解决贝叶斯分类时，首先根据已知的先验概率获得各个类标号未知类的条件概率分布，然后利用某种抽样器，分别得到满足这些条件分布的随机数据，最后统计这些随机数据，就可以得到各个类标号未知类的后验概率分布。