bayes classifier中文是什么意思

• 贝斯分类符
• 贝叶斯分类器

• "bay"中文翻译    n. 堤防，河堤。 vt. 筑堤遏(水)。 bay wa ...
• "classifier"中文翻译    n. 1.分类者。 2.【矿物】分级机。 3.【化学】 ...
• "bayes" 中文翻译 :    巴耶斯; 贝斯; 贝叶斯
• "classifier" 中文翻译 :    n. 1.分类者。 2.【矿物】分级机。 3.【化学】分粒器。 4.(汉语等中的)量词。
• "bayes bayesian" 中文翻译 :    贝叶斯定理的
• "bayes classification" 中文翻译 :    贝斯分类; 贝叶斯分类
• "bayes estimate" 中文翻译 :    贝斯估值
• "bayes formula" 中文翻译 :    贝斯公式; 贝叶斯公式
• "bayes law" 中文翻译 :    贝叶斯定律
• "bayes machine" 中文翻译 :    贝斯机器; 贝叶斯机器
• "bayes postulate" 中文翻译 :    贝叶斯公设
• "bayes principle" 中文翻译 :    贝叶斯原理
• "bayes risk" 中文翻译 :    贝叶斯风险
• "bayes rule" 中文翻译 :    贝斯法则; 贝叶斯法则
• "bayes solution" 中文翻译 :    贝叶斯解
• "bayes test" 中文翻译 :    贝斯检验; 贝叶斯检验
• "bayes theorem" 中文翻译 :    贝叶斯定理
• "bayes theory" 中文翻译 :    贝叶斯定理
• "naive bayes" 中文翻译 :    朴素贝叶斯
• "peter bayes" 中文翻译 :    柏尔斯
• "admissibility of bayes decision rule" 中文翻译 :    贝叶斯决策规则的宜取性
• "bayes classification rule" 中文翻译 :    贝斯分类规则; 贝叶斯分类规则
• "bayes decision function" 中文翻译 :    贝叶斯判定函数
• "bayes decision procedure" 中文翻译 :    贝叶斯决策过程
• "bayes decision rule" 中文翻译 :    贝斯决策规划; 贝斯判定规则; 贝叶斯决策规则

• Mixed naive bayes classifier model
  混合式朴素贝叶斯分类模型
• Research on the method of processing empty value based on generalized naive bayes classifiers
  基于广义朴素贝叶斯分类器的空值处理方法
• Author develop three text dassiliers lilie naive - bayes classifier , k nearest neighbor classifier and svm classifier . furthermore , including the three classifiers , one text categorisation system is built up , and it has high prachcability
  作者采用三个模型，实现了朴素贝叶斯分类器、 k近邻分类器和支持向量机分类器三个中文文本分类器，集成了一个实用性较强的实验系统。
• This study employed six data mining methods , including logistic regression , discriminant analysis , artificial neural networks , k - nearest - neighbors , na ? ve bayes classifier , and classification trees , to find the most important factors of earthquake - caused landslide
  本研究利用六种资料探勘方法，包括逻辑回归、判别分析、类神经网络、最近邻法、贝氏分类器、分类树，探讨影响地震引起山崩的重要因子。
• Along the medial axes with the same steps , we can get the average integral gray profile and the width profile . 3 . karyotype classification of chromosome : according to the chromosome ’ s geometrical parameters , the karyotype classification are realized through the normal bayes classifier or fuzzy clustering based on the character of average integral gray profile
  利用染色体的中线等步长提取平均积分轮廓和宽度轮廓; 3 、染色体的核型分类:利用染色体结构参数即其灰度轮廓通过正态贝叶斯分类器或采用模糊聚类的方法来实现染色体的对号分类。
• Employing idef to establish framework , dynamitic and functional models , the system also designs a non - back shifting search arithmetic for double - scanning buffer zone and a double - track structure for searching process . according to the characteristics of e - mail control and electronic documentary mining technology , bayes classifiers are made to strengthen the electronic control system in which electronic documentary mining technology is used ; and moreover the double systematic structure of c / s & b / s is constructor with the presence of some function relationships in mining process as well as systematic mining and program handling
  系统采用i _ 2def方法建立了结构模型、动态模型和功能模型；设计了双扫描缓冲区的无回溯搜索算法及搜索过程的双栈结构；根据电子邮件监控系统和电子文档挖掘技术的特征，设计了bayes分类器并使用了增强型方法，提出了一种运用电子文档挖掘技术的电子邮件监控系统；构建了c / s和b / s双重体系结构；并给出了挖掘过程的部分函数调用关系及系统挖掘的处理过程、部分处理程序。
• Classification has always been a central issue on data mining , machine learning and pattern recognition , classifier , as an important model and method of machine learning and data mining , is very important to the development and application of machine learning and the data mining . the classifier ’ s effect closely correlates with the characteristic of data sets , at present , the construction of classifier is generally based on the character of different datasets , there is no such a classifier which is suitable for any data sets . under uncertain conditions , the bayes network is a powerful tools for the knowledge expression and inference , but for difficulties in constructing its network structure and very high time complexity , it has not been considered as a classifier algorithm until the emergence of na ? ve - bayes classifier
  分类一直是数据挖掘、机器学习和模式识别等研究的核心问题,贝叶斯网络是作为知识表示和推理的强大工具,由于搜索空间巨大和学习困难的原因,直到朴素贝叶斯理论的出现才被作为分类器算法,改进朴素贝叶斯分类器是贝叶斯分类器学习的一个主要的研究方向。遗传算法本质上是一种求解问题的高效并行全局搜索算法,适合应用于那些改进的分类器的结构学习中。本文提出了一种基于遗传算法的ban分类器算法。