数字识别的支持向量机方法

摘要............................................................ - 1 - Abstract........................................................ - 2 - 第一章绪论..................................................... - 3 -

1.1研究背景................................................. - 3 -

1.1.1数字识别概述....................................... - 3 -

1.1.2数字识别的问题和困难............................... - 3 - 第二章数字识别技术............................................. - 4 -

2.1数字识别的方法........................................... - 4 -

2.1.1神经网络的BP学习算法.............................. - 4 -

2.1.2 SVM算法........................................... - 5 -

2.2支持向量机与多层前向网络区别............................. - 6 - 第三章统计学理论与支持向量机................................... - 7 -

3.1统计学习理论的核心内容................................... - 7 -

3.1.1VC维............................................... - 7 -

3.1.2推广能力的界....................................... - 7 -

3.1.3结构构风险最小化................................... - 8 -

3.2线性支持向量机........................................... - 8 -

3.3非线性支持向量机........................................ - 11 -

3.4支持向量机的评价........................................ - 11 - 第四章数字识别的支持向量机的方法.............................. - 13 -

4.1 SVM学习算法步骤........................................ - 13 -

4.2应用SVM算法识别数字字符和结果分析.......... 错误！未定义书签。

4.2.1数字识别的实现.................................... - 14 -

4.2.2 识别结果分析...................................... - 15 -

4.3支持向量机与多层前向网络BP算法结果的比较............... - 15 - 第五章结论.................................................... - 17 - 致谢........................................................... - 18 - 参考文献....................................................... - 19 - 附录........................................................... - 20 -

摘要

数字作为世界经济发展的信息的载体，利用计算机数字识别和文档处理技术成为人们迫切要解决的问题。本文先介绍数字识别和数字识别的几种技术，主要包括多层神经网络BP 算法和支持向量机等,并对这两种方法的比较，找出他们的区别。接下来介绍支持向量机的工作原理以及在其图像识别中的应用，指出了该方法与BP识别法的优势所在，并在以数字字符的识别为例进行实现，通过把字符图像转化为数据矩阵，并MATLAB下给出识别结果，实验结果表明了该方法识别准确性较高，而且SVM（support ector machine）样本训练的收敛速度比较快。

关键词：数字识别支持向量机数据矩阵 MATLAB

Abstract

Digital as the development of the world economy by computer information carrier, digital identification and document processing technology to become people urgent problems to solve. This paper first introduces digital identification and several kinds of numeral recognition technology, mainly including multilayer neural network based on BP algorithm and support vector machine, etc, and on the comparison of the two methods, find out their differences. Next the introduced support vector machine working principle and its application in image recognition, points out the method and BP recognition method advantage, and, in the digital character recognition as an example, through the chatacter image into data matrix and MATLAB recognition result gives, and experimental results show that the new method is accurate, and the SVM (support ector machine) sample training convergence speed is faster.

The keywords：digital identification support ector machine data matrix matlab