[1]檀何凤,刘政怡. 模式分类方法比较研究[J].计算机技术与发展,2015,25(02):99-102.
　TAN He-feng,LIU Zheng-yi. Research on Comparison of Pattern Classification Method[J].,2015,25(02):99-102.
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 模式分类方法比较研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年02期
页码:
99-102
栏目:
智能、算法、系统工程
出版日期:
2015-02-10

文章信息/Info

Title:
 Research on Comparison of Pattern Classification Method
文章编号:
1673-629X（2015)02-0099-04
作者:
 檀何凤; 刘政怡
 安徽大学 计算机科学与技术学院
Author(s):
 TAN He-feng;  LIU Zheng-yi
关键词:
 ; 模式分类; UCI数据库; 数据分类; 模式识别
Keywords:
 ; pattern classification; UCI database; data classification; pattern recognition
分类号:
TP39
文献标志码:
A
摘要:
 模式分类方法是模式识别的关键。文中重点研究了支持向量机、BP神经网络、K近邻、朴素贝叶斯、线性判别分析和二次判别分析共六种模式分类方法，并利用MATLAB对UCI上的数据集进行了分类测试，根据测试结果分析了数据集的样本数、特征数、类别数对每一种模式分类方法的准确率和运行时间的影响。结果表明，在对一些小数据进行分类时，可以采用朴素贝叶斯、K近邻、线性判别分析方法，而对于大的数据集，支持向量机、BP神经网络、二次判别分析分类方法则比较适合，但对运行时间要求严格的分类不能采用BP神经网络方法。
Abstract:
 Pattern classification method is the key to pattern recognition. It focuses on six pattern classification methods,including support vector machine,BP neural network,k-nearest neighbor,naive Bayes,linear discriminant analysis and quadratic discriminant analysis,and tested by using the MATLAB on UCI data sets. According to the results,analyze the influence of the number of samples,features,catego-ries from data sets to the accuracy and running time of six classification methods. The results showed that naive Bayes,k-nearest neigh-bor,linear discriminant analysis are suitable for small data sets,support vector machine,BP neural network,quadratic discriminant analysis for large data sets. However,when it is strict to the running time,cannot use the BP neural network.

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更新日期/Last Update: 2015-04-28