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   贝叶斯分类 在 宏观经济管理与可持续发展 分类中 的翻译结果: 查询用时:0.514秒
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贝叶斯分类
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  bayesian classification
    This paper mainly deals with the multivariate Bayesian inference theory used in the modern economical and management science. This includes the Bayesian inference theory about three important kinds of linear models, including the single equation model, multiple equation model system and VAR(p) predictive model, and their application in economic forecasting and quality control, and also the design for the Bayesian classification identification method among multiple populations.
    本文主要研究现代经济管理中的多元贝叶斯推断理论,包括单方程模型、多方程模型系统和向量自回归VAR(p)模型的贝叶斯推断理论及其在经济预测与质量控制中的应用,以及多总体的贝叶斯分类识别方法的构造。
短句来源
    Classification discovery is an important task in Data Mining,and the Bayesian classification and the decision tree in data mining are applied quite wildly. They are the two effective methods to produce the sorter.
    分类发现是数据挖掘的重要内容,贝叶斯分类和决策树在数据挖掘中应用相当广泛,它们是生成分类器的两种有效方法。
短句来源
    The naive Bayesian classification method is used to forecast new or potential customers' value,and a relevant customer development strategy is thus available.
    利用朴素贝叶斯分类方法来预测新客户(或潜在客户)的价值,并依据预测结果来制定相应的重点客户发展战略.
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  “贝叶斯分类”译为未确定词的双语例句
    Bayesian Linear Inference Theory and Classification Identification Method for Multiple Populations in the Modern Economics and Management
    现代经济管理中的线性贝叶斯推断理论与多总体贝叶斯分类识别方法研究
短句来源
    The thesis have studied the Database Marketing (DBM) application in real estate enterprise, which is based on analyzing and studying DBM application in other industries, Customer Relationship Management in real estate enterprise, and combines Relationship Marketing and Direct Marketing .
    通过借鉴数据库营销在其他行业的应用经验以及对客户关系管理在房地产企业的应用的分析和研究,结合关系营销和直复营销,研究了数据库营销在房地产企业的应用,具体研究内容如下:对贝叶斯分类方法在目标客户发现中的应用作了研究。
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  bayesian classification
These are including the power spectral analysis methods, the texture analysis, traditional Bayesian classification theory and the most active neural network approaches.
      
Clinical Diagnosis Based on Bayesian Classification of Functional Magnetic-Resonance Data
      
In the AdaBoost algorithm, we employ Bayesian classification to replace the traditional binary weak classifiers to enhance their classification power, thus producing a stronger classifier.
      
The fractal features determined by both approaches are used to design a Bayesian classification scheme.
      
Bayesian classification is currently of considerable interest.
      
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Classification discovery is an important task in Data Mining,and the Bayesian classification and the decision tree in data mining are applied quite wildly.They are the two effective methods to produce the sorter.This article separately uses two methods to classify and forecast the customer degree of satisfaction,and compares and analyze the two methods,Decision tree is considered to forecast the customer degree of satisfaction its characteristic of simplicity and efficiency.

分类发现是数据挖掘的重要内容,贝叶斯分类和决策树在数据挖掘中应用相当广泛,它们是生成分类器的两种有效方法。文章分别用两种方法对顾客满意度进行分类及预测,并将两种方法进行比较分析,认为用决策树分类法来预测顾客满意度具有简洁、高效等特点。

A new method to forecast customers' value is put forward using such data mining techniques as clustering and classification.The indicators reflecting old customers' value and business integrity are gained through analyzing the historical transaction data.Then,these old customers are clustered and further classified into different groups in accordance to their value indicators,i.e.,each and every old customer is assigned with a mark equivalent to its value.The naive Bayesian classification method is used to forecast...

A new method to forecast customers' value is put forward using such data mining techniques as clustering and classification.The indicators reflecting old customers' value and business integrity are gained through analyzing the historical transaction data.Then,these old customers are clustered and further classified into different groups in accordance to their value indicators,i.e.,each and every old customer is assigned with a mark equivalent to its value.The naive Bayesian classification method is used to forecast new or potential customers' value,and a relevant customer development strategy is thus available.A numerical example is given to verify the effectiveness and practicability of the method proposed.

提出了一种利用聚类和分类等数据挖掘技术预测客户价值的新方法.通过对客户历史交易数据的分析,获得能够综合反映老客户忠诚度和价值度的指标.基于该指标对老客户进行聚类,将老客户划分为若干个不同价值的客户群,即为每个老客户赋予一个价值等级标号.利用朴素贝叶斯分类方法来预测新客户(或潜在客户)的价值,并依据预测结果来制定相应的重点客户发展战略.实例验证了该方法的有效性和可行性.

 
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