参考资料--基于支持向量机的模式识别方法研究2.docxVIP

摘要支持向量机(Support Vector Machine简称SVM)方法，是在统计学习理论的基础上发展起来的一种新的机器学习方法，它是建立在统计学习理论的VC维理论和结构风险最小化原则上的，避免了局部极小点，并能有效地解决过学习问题，具有良好的推广性能和较好的分类精确性。本文介绍了统计学习理论、支持向量机的原理、支持向量机研究现状，并在MATLAB软件工具环境下，对于具体的模式识别问题：乳腺组织电阻抗特性的乳腺癌诊断，进行实验仿真和性能分析。实验结果表明，基于支持向量机的模式识别方法能够真确有效的诊断出乳腺疾病，尤其对于二分类问题，SVM更有其优越性。关键词：支持向量机；模式识别；统计学习理论AbstractSVM (Support vector Machine SVM) method, which is part of the statistical learning theory is one of the youngest and also the most practical part. Its core content is between 1992 and 1995, at present is still in development stage. Statistical learning theory is, as it were, since the 1990 s by more and more seriously, largely because of the development of the support vector machine (SVM) is a general learning algorithm. Because in a sense the SVM can be expressed as similar to neural network in the form of, so the SVM in the original is also called support vector networks.Statistical learning theory has been introduced in this paper, the principle of support vector machine (SVM), support vector machine (SVM), and software tools in the MATLAB environment, for a specific pattern recognition problems: electrical impedance characteristics of breast cancer diagnosis of mammary gland, experiment simulation and performance analysis. The experimental results show that the pattern recognition based on support vector machine (SVM) method can correct the diagnosis of mammary gland disease effectively, especially for binary classification problems, SVM has more advantages.Keywords:Support vector machine;Pattern recognition;Statistical learning theory目录第1章概述11.1 支持向量机产生背景11.2 支持向量机的研究现状21.3 本文研究内容3第2章统计学习理论52.1 机器学习的基本问题52.2 统计学习理论82.3本章小结10第3章支持向量机基本理论113.1 线性可分情况113.2 线性不可分情况143.3 支持向量机的核函数153.4 支持向量机的训练步骤163.5 支持向量机常用算法163.6 本章小结19第4章基于支持向量机的模式识别方法实验分析204.1 问题描述204.2 仿真实验254.3 本章小结28结论29参考文献30致谢31附录MATLAB仿真程序32第1章概述支持向量机是在统计学习理论的基础上发展起来的一种新的机器学习方法，它是建立在统计学习理论的VC维理论和结构风险最小化原则上的，避免了局部极小点，并能有效地解决过学习问题，具有良好的推广性能和较好的分类精确性。支持向量机在解决小样本、非线性及高维模式识别问题中表现出的许多特有的优势，使它成为一种优秀的机器学习算法[1