支持向量机在人口数据分析中的应用

发布时间：2019-06-04 07:27

【摘要】：统计学习理论是针对于小样本下的机器学习理论,它的核心思想是通过控制学习机的复杂度实现对其推广能力的控制。根据这一理论发展起来的支持向量机是以VC维和结构风险最小化原则为基础,支持向量机具有很多优点,它的出现较好地解决了过学习、非线性、高维数等实际问题。现如今,支持向量机被应用到生活中的各个领域,解决一些现实问题。本文主要介绍支持向量机的特点以及在人口数据分析当中的实际应用。文章绪论主要简单的阐述了选题的背景以及研究意义,并且介绍了国内外对支持向量机的研究现状。本文第二章简单地介绍了机器学习的发展历史及与机器学习相关的问题。第三章简述统计学理论,包括统计学习主要内容,VC维、推广性的界、结构风向最小化原则等相关概念及内容。第四章重点介绍支持向量机的相关内容,包括线性支持向量机和非线性支持向量机,线性支持向量机这部分又细分成线性可分和线性不可分这两种情况,这一章还介绍了核函数的相关概念以及支持向量机回归机。第五章论述了支持向量机的相关特点及其所具有的优势。第六章是本文的重点内容,根据收集得到的有关沈阳市2002年到2014年年末人口总数作为数据,用支持向量机建立模型进行人口预测,本章建立了两种支持向量机模型,预测出未来五年内沈阳市年末总人口数量。第七章根据收集得到沈阳市地区生产总值,找到人口数量与沈阳地区生产总值之间的关系,说明预测人口数量的重要性。最后,总结支持向量机方法的特点,对支持向量机今后的发展进行展望并且提出今后的研究方向。
[Abstract]:Statistical learning theory is aimed at the machine learning theory under the small sample. Its core idea is to control the generalization ability of the learning machine by controlling the complexity of the learning machine. The support vector machine developed according to this theory is based on the principle of VC and structural risk minimization. support vector machine has many advantages, and its appearance solves the practical problems such as overlearning, nonlinear, high dimension and so on. Nowadays, support vector machine (SVM) is applied to all fields of life to solve some practical problems. This paper mainly introduces the characteristics of support vector machine and its practical application in population data analysis. In the introduction, the background and significance of the topic are briefly described, and the research status of support vector machine at home and abroad is introduced. The second chapter briefly introduces the development history of machine learning and the problems related to machine learning. The third chapter briefly describes the statistical theory, including the main contents of statistical learning, VC dimension, generalization boundary, structural wind direction minimizing principle and other related concepts and contents. The fourth chapter focuses on the related contents of support vector machine, including linear support vector machine and nonlinear support vector machine, linear support vector machine is divided into linear divisible and linear inseparable cases. This chapter also introduces the related concepts of kernel function and support vector machine regression machine. The fifth chapter discusses the related characteristics of support vector machine and its advantages. The sixth chapter is the key content of this paper. According to the collected data about the total population of Shenyang from 2002 to the end of 2014, the population prediction is carried out by using support vector machine (SVM) model, and two kinds of support vector machine models are established in this chapter. Forecast the total population of Shenyang at the end of the year in the next five years. In the seventh chapter, according to the collection of the gross domestic product of Shenyang area, the relationship between the population quantity and the gross domestic product of Shenyang area is found, and the importance of forecasting the population quantity is explained. Finally, the characteristics of support vector machine method are summarized, the future development of support vector machine is prospected and the future research direction is put forward.
【学位授予单位】：辽宁师范大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：C921

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