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1、摘 要本文论述了支持向量机的基本原理和思想，侧重分析了支持向量机的两种多类分类训练算法（OAA算法、OAO算法）和四种核函数（即线性内核、径向基函数内核、多项式内核，S型内核）。本论文实现了三种算法（还包括BSVM算法），并使用这三种训练算法和这四种核函数实现了非线性模式分析模型。本论文利用matlab程序语言，实现了一个图形界面。该界面能使用户灵活的选择三种训练算法的一种和四个核函数的一个对数据集进行分类，并能手动创建数据和装载已有数据，还能清除已创建或装载的数据。对于分类结果，图形界面还能显示出分类所用的训练算法，核函数，算法训练时间，支持向量个数以及分类错误率。根据算法训练时间，支持向量
2、个数和分类错误率的统计，本文还做成三个表格，简单分析了三种算法的优劣。本论文的创新点在于：原有算法只能使用一种核函数和一种C调整系数来修正算法的分类效果，本论文通过收集核函数的资料，扩展到能使用四种核函数，并修改少量代码使算法能使用两种C调整系数。关键词：支持向量机，多类分类，核函数AbstractThis text has expounded the basic principle and thought of Support Vector Machine, and has laid particular emphasi on analyzing two kinds of Multi-cla
3、ss classification training algorithms (OAA algorithm, OAO algorithm) and four kernel functions(Linear, Polynomial, RBF, sigmoid) of Support Vector Machine. This thesis implement the three training algorithms（include , BSVM algorithm）, and then, implement the non_linear pattern analysis model. This t
4、hesis implement a graphical interfaces using matlab program language. It is convenient to choose one of the three training algorithms and one of the three kernel functions to classify the data by the graphical interfaces. And the graphical interfaces implement the function of creating data sets by u
5、ser and loading the existent data sets. Moreover, the graphical interfaces can clear the data sets which is created and loaded by user. For the result of classification, the graphical interfaces can display the sort of training algorithms and kernel functions, the time algorithm training, the number
6、 of support vector and the error ratio of classification. This text have made three table by the stat of the time algorithm training, the number of support vector and the error ratio of classification, and analyse simply the quality of the three training algorithms. This thesis have some innovation.
7、 The former algorithm can use a sort of kernel function and one kind of C parameter. And now, the algorithm can use four kernel functions by collecting information about kernel function and can use two C parameters by modifying a few codes. Key words: Support Vector Machine, Multi-class classificati
8、on, kernel function目 录摘 要IAbstractI目 录I第一章 绪 论11.1研究背景11.2国内外研究状况及成果41.3研究目的51.4开发工具简介5第二章 支持向量机82.1支持向量机的思想82.2支持向量机的方法82.3支持向量机现有的几种应用132.4支持向量机的特点132.5支持向量机的核函数14第三章 分类及多类分类问题163.1分类问题和分类学习机163.2多类分类问题的描叙183.3多类分类具体算法193.3.1OAA算法193.3.2OAO算法21第四章 非线性模式分析模型的总体设计234.1功能需求分析234.2总体设计234.3软件具体设计264.3
9、.1.软件功能模块的划分264.3.2.系统的构架274.3.3.模型的总流程图28第五章 非线性模式分析模型的实现295.1模型功能描述295.2系统关键代码305.3系统运行结果33第六章 非线性分类模型分析386.1分类图形结果386.2分类结果分析44结 论47致 谢48参考文献49附录 设计系统部分源代码52II正文内容：除了一、二、三、四级标题外，正文文本为宋体小四号字，20磅行距。中文文本中的括号、标点符号为中文半角状态下的括号、标点符号，英文的括号、标点符号为英文半角状态下的括号、标点符号。图、表的题头为黑体五号字、其中的文本为宋体五号字，图、表所在段落为单倍行距。正文页码：阿
10、拉伯数字，宋体小五号字，页面底端居中（默认格式）。ContentsABSTRACT. ISECTION. IINTRODUCTION. ISECTION I INTRODUCTION. 1Chapter Research Project. 11.2 The status and results of four domestic and foreign research. 41.3 The purpose of the research. 51.4 Introduction 5 Development Tools. 5Chapter I I Support Vector Machine. 82.
11、1 Support Vector Machines thought. 82.2 The support vector machine method. 82.3 Support vector machine several existing applications. 132.4 Support vector machine features. 132.5 Support Vector Machine Kernel. 14Chapter III classification and multi-class classification. 163.1 Classification and classification learning machine. 163.2 classification problems depicts. 183.3 specific algorithm for classification. 193.3.1 OAA algorithm. 193.3.2 OAO algorithm. 21Chapter IV Nonlinear model of Chapter of the model design. 234.1 Functional Requirements Analysis.
