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Search Results: 1 - 10 of 30977 matches for " 费宇 "
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基于神经网络的昆明市牛奶品牌选择预测研究
Kunming Milk Brand Choice Prediction Research Based on Neural Network
 [PDF]

李海燕,
Hans Journal of Data Mining (HJDM) , 2015, DOI: 10.12677/HJDM.2015.51001
Abstract:
随着生活条件的提高,人们越来越注重饮食的健康,品牌意识越来越强。本文以牛奶品牌为例,研究了消费者如何进行品牌选择,讨论了消费者特征与最终选择的牛奶品牌之间的联系,结果显示品牌对于消费者的行为有较大的影响,消费者的性别、年龄、收入、文化程度、家庭结构与最终的品牌选择有很强的联系,可以采用BP神经网络模型来拟合这种关系,模型用于回判分析以及三折交叉检验均达到较好效果,准确率80%左右,为牛奶生产者与营销者提供一定的参考。
With the improvement of living conditions, people pay more and more attention to the healthy diet, and their brand consciousness is stronger too. Taking the milk brand for an example, this paper studies the consumers how to make brand choice and discusses the connection between the con-sumer characteristics and the final choice of milk brand. The result shows that the brand has large influence on consumer behavior. Consumers’ gender, age, income, educational level and family structure have strong links with the final brand choice. And the BP neural network model fits the relationship well. Two tests of the model show about 80% of accuracy. The conclusion provides certain reference for milk producers and sellers.
基于小波变换的沪深300指数预测
Hushen 300 Index Price Forecasting Based on Wavelet Transform
 [PDF]

汪思慧,
Statistics and Applications (SA) , 2014, DOI: 10.12677/SA.2014.34024
Abstract:
本文通过基于小波变换和未基于小波变换对沪深300指数日收盘价序列分别建立ARMA拟合模型并做短期预测,对其归一化均方误差(NMSE)进行比较,结果显示,由于小波变换良好的时频局域化特性,以及它的多分辨功能,使组合模型较之于单个预测模型对于沪深300指数的短期预测更优。
This paper based on wavelet transform and non-wavelet transform established ARMA models to fit the daily closing price of Shanghai and Shenzhen 300 index and do a short-term forecast. It also compared their normalized mean square error (NMSE). Results display that due to the characte-ristics of wavelet transform which are good time frequency localization and its multi-resolution features, combined forecast model for short-term forecast of Shanghai and Shenzhen 300 index is superior to single forecast model.
基于广义线性混合模型的电信客户流失预测研究
A Study of Telecom Customer Loss Prediction Based on Generalized Linear Mixed Models
 [PDF]

王珺, , 潘建新
Statistics and Applications (SA) , 2013, DOI: 10.12677/SA.2013.21006
Abstract:

随着通讯业务的竞争日趋激烈,客户关系管理的重要性日益突出,如何提高客户满意度、减少客户流失几率成为电信企业提高竞争力的重要策略。本文在总结国内外学者研究的基础上,采用广义线性混合模型分析客户流失原因,进行客户流失预测,为电信企业提供一定的参考。
With the increasingly fierce competition in the communication business and the growing importance of customer relationship management, how to improve the customer satisfaction and reduce the customer churn rate has became the main strategy to improve the competitiveness of telecom enterprises. Based on the summaries of former researches, this paper used the Generalized Linear Mixed Model to analyze the reasons of customer loss, find out the customer loss prediction model and provide several references for the telecom enterprises.





基于财务数据的小额信贷决策模型
The Study of Jot Loan Decision Model Based on Financial Information
 [PDF]

胡淙海, , 高正捷
Hans Journal of Data Mining (HJDM) , 2015, DOI: 10.12677/HJDM.2015.52004
Abstract:
本文利用Logistic回归分析不同贷款申请企业的还款概率,再通过最小误判代价准则确定可接受的还款概率,在此基础上构建最小误判代价(minimum misclassification cost)模型及改进的最小误判代价(advanced minimum misclassification cost)模型,通过对XY小额信贷公司的历史贷款数据进行分析,可以发现两个模型相对XY小额信贷公司采用的贷款收入比率(rate of debt and income)模型,有了较大的改进,在降低风险提高收益的同时,还能针对不同风险水平对贷款行为进行一定地调整,并且改进的最小误判代价模型还能对贷款金额进行一定的调整。
In this paper, we use Logistic Regression to analyze the repayment probability of different enter-prises, and then we determine appropriate threshold repayment probability by the minimum cost criterion. On this basis, we build the minimum miscalculation cost model and advanced minimum misclassification cost model. After analyzing the XY jot credit company’s history data, comparing with the rate of debt and income model which is being used by XY Company, we find that these two models have more advantages. They can not only reduce the risk of increase in revenue, but also make adjustment according to different levels of risk behavior; besides, the advanced minimum misclassification cost model can adjust the loan amounts.
融合粗糙集与灰色理论的电力变压器故障预测
胜巍,
中国电机工程学报 , 2008,
Abstract: 结合粗糙集和灰色理论的各自特点,提出一种用于变压器故障预测的新方法。基于粗糙集的知识获取方法,获得改进的三比值诊断决策表,并简化决策表,建立最小诊断规则;分别建立决策表中三比值的灰色预测模型,通过灰色模型对特征气体的比值进行预测,获得预测的特征气体比值的状态特征,对照最小诊断规则,得出预测的故障类型,并结合规则置信度和预测的状态特征对该故障类型的支持数确定其发生概率,这种方法通过结合预测气体比值状态特征和诊断规则,尽早准确地发现潜伏的故障类型,从而可以预先有针对性对变压器进行检修;进行变压器故障预测实例分析,预测结果证明该方法的有效性和正确性。
国际草药CONSORT声明及中药临床试验报告规范化问题思考
,刘建平
中国中药杂志 , 2008,
Abstract: 通过对国际草药CONSORT声明内容的详细说明,结合中药临床试验特点,分析中药临床试验报告规范化问题。中药临床试验报告规范化问题已经引起广泛注意,但是至今尚无统一标准。与此最为相关的是2006年CONSORT小组出台的《草药随机对照临床试验的报告:CONSORT声明细则》。该声明包括5部分,共22项条目,重点对试验报告中受试者标准、干预措施、对照设置、结局指标设置等内容的报告制定了详细的说明。虽然,鉴于中药具备的中医理论背景和其中包括的动物、矿物药材,所以中药临床试验的报告不宜完全按照《国际草药CONSORT声明》来进行规范,但是《草药CONSORT声明》的出台为下一步制定专门的中药临床试验报告规范提供了有益参考。中药的临床试验设计和报告应该根据不同的中药类型和试验目的制定不同标准。以中医药理论指导临床应用的中药,其临床试验的报告应体现辨证论治内容;从中药中经过提纯而成的类似于现代化学药品的药物,其临床试验可以在很大程度上参照《国际草药CONSORT声明》来报告。
基于最小二乘支持向量机的森林火灾预测研究
Prediction of Forest Fires Based on Least Squares Support Vector Machine
 [PDF]

李恩来,
Hans Journal of Data Mining (HJDM) , 2016, DOI: 10.12677/HJDM.2016.61003
Abstract:
森林火灾是一个主要的环境问题,造成经济损失和生态破坏而且危及生命。如何预测、防治或减少森林火灾的危害成为诸多学科领域共同关注的科学任务。传统的做法是使用卫星,红外线扫描仪和局部传感器。但是由于卫星定位的延迟和扫描仪高昂的设备成本和维护成本,这些方案不能用来解决所有的情况。然而,研究表明气象因素对森林火灾有重要的影响。因此,有不少的学者建立森林火灾预测系统并将气象数据纳入量化指标体系。随机计算机的迅速发展,不少的学者将机器学习的方法运用到森林火灾等级预测模型中,但是其预测效果并不十分理想。本文提出基于机器学习中支持向量机方法的改进方法-最小二乘支持向量机,由于最小二乘支持向量机对处理样本容量较小的数据具有较高的准确度而且耗时较短。本文选用UCI数据库中的森林火灾数据进行预测处理,选用高斯函数(径向基函数)作为最小二乘支持向量机的核函数,根据一对一的多分类算法设计出最小二乘支持向量机的多分类器,使用粒子群算法选择最优参数。最后与支持向量机、BP神经网络、决策树等方法进行对比。
Forest fire is a major environmental problem, resulting in economic loss and ecological damage, and endangering life. How to predict, prevent or reduce the damage of forest fire has become a scientific task of many disciplines. The traditional approach is to use a satellite, an infrared scanner, and a local sensor. However, due to the delay of the satellite positioning and the high cost of the scanner’s equipment and maintenance costs, these solutions can not be used to solve all the situation. However, the study shows that the meteorological factors have an important influence on forest fire. Therefore, many scholars have established system for forest fire prediction and the meteorological data into the quantitative index system. With the rapid development of random computer, many scholars have applied the method of machine learning to forest fire grade prediction model, but the effect is not very ideal. This paper presents an improved method of support vector machine method based on machine learning, because the least squares support vector machine is with a higher accuracy and shorter time consuming to process small sample size of the data. In this paper, we select the UCI database of forest fire forecast data processing, select Gaussian function (radial basis function) as the kernel function of least squares support vector machine, according to one of multiple classification algorithm design of least squares support vector machine classifier, using particle swarm optimization algorithm to choose the optimal parameters. Finally, it is compared with the support vector machine, BP neural network, decision tree and so on.
基于机器学习分类方法的信用卡审批应用
The Application of Credit Approval Based on Machine Learning Classification Method
 [PDF]

莫玉莲,
Hans Journal of Data Mining (HJDM) , 2016, DOI: 10.12677/HJDM.2016.63012
Abstract:
传统的信用卡审批方法往往是依靠信贷人员的经验进行审批,确定信用卡申请者是否符合申请条件,这种审批方法有很大的随意性和不稳定性。本文利用R软件并将最新的六种机器学习分类方法——决策树分类、Adaboost分类、Bagging分类、随机森林分类、支持向量机分类、人工神经网络引入到信用卡申请管理中,建立了自动化的申请管理体系,有效地降低了审批结果的随意性和不稳定性,并通过八折交叉验证计算出每种方法的分类均方误差并进行对比,筛选出分类效果最好的方法。结果表明:随机森林分类的分类误差是最小的。
The traditional method of credit card approval is often rely on the experience of credit personnel and is to decide whether the credit card applicants meet the conditions of application. Obviously, this approval method has a lot of randomness and instability. In this paper, we take advantages of R software and introduce the six latest machine learning classification method, decision tree clas-sification, AdaBoost, Bagging classification, random forest classifier, support vector machine (SVM) classification, artificial neural network (Ann) into the credit card application management, then establish the automatic application management system, effectively reducing the randomness and instability of the examination and approval results. Finally we calculate the mean square error of all the classification method through 8-fold cross validation and chose the classification with the best effect. The result shows that the classification error of random forest classification is the smallest.
影响不同种族患高血压的因素分析
Analysis of Factors Influencing Hypertension in Different Ethnic Groups
 [PDF]

胡梦婷,
Hans Journal of Data Mining (HJDM) , 2016, DOI: 10.12677/HJDM.2016.63013
Abstract:
由于现在人们得高血压的几率越来越大,而高血压所引起的并发症十分危险,高血压各种并发症病逐渐成为了现代健康杀手之一。本文选用UCI数据库中的美国健康普查数据进行分析处理。本文通过对不同种族的人的各因素用logit分类以及随机森林分类进行分析,得到了以下结论:不论种族,年龄对高血压都有显著影响;对于不同种族,其他的因素对高血压的影响程度不同。
Recently, it is more likely to have high blood pressure, and complications related to high blood pressure are dangerous. Complications of hypertension have gradually become one of the killers of modern health. In this paper, we use the United States health survey data in UCI database for analysis and processing. We dealt with each factor of people from different races using Logit Clas-sification and the Random Forest Classification, and obtained the following conclusions: Regardless of race, age had significant effects on high blood pressure; For different ethnic groups, influence of other factors on hypertension is different.
住宅商品房市场细分—基于A市居民购房意向调研
Residential Real Estate Market Segmentation—Based on Residents Purchase Intention Survey of A City
 [PDF]

常思敏,
Modern Marketing (MOM) , 2016, DOI: 10.12677/MOM.2016.63006
Abstract:
为了更好地了解消费者的购房需求,细分住宅商品房市场,为房地产商制定精准的市场营销策略提供借鉴,本文将传统的统计学方法和机器学习的方法相结合定量分析了住宅商品房市场的需求状况。首先,文章结合以往研究经验选取房地产市场细分指标,通过问卷调查获取A市居民的购房意向与需求信息。然后,采用K-means聚类分析和决策树分类方法将消费者分为四类。最后,根据分类结果,运用频数分析、百分比分析、交叉列联分析方法得出了每一个类别的市场特征和需求倾向,为房地产开发商精准营销提供借鉴。同时,本文还得出了四类市场群体在住宅户型、购房关注点等方面的选择共性,为房地产开发商初期项目建设也有一定的指导意义。
In order to better understand the housing demand of consumer, divide the residential real estate market, and provide reference for precise marketing strategies for real estate developers, this paper, combining the traditional statistical methods and machine learning method, analyzes the residential housing market demand in quantitative way. First of all, this paper bases previous re-search experience to select indicators for the real estate market segmentation, through the ques-tionnaire survey to obtain A city’s information about residential purchase intention and demand. Then, through using the K-means cluster analysis and decision tree classification method, it comes to the conclusion that consumers can be divided into four groups. Finally, according to the classi-fication results, and using frequency, percentage, cross contingency analysis methods, it shows demand characteristics and tendencies of each category. All of these provide reference for real estate developers in their project preparation stage.
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