[1]王刚刚[],等. 基于标准误差的最小二乘线性分类器[J].计算机技术与发展,2017,27(10):78-82.
　WANG Gang-gang[],ZHAO Li-feng[],XIE Ya-li[]. A Least Square Linear Classifier with Standard Error[J].,2017,27(10):78-82.
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 基于标准误差的最小二乘线性分类器()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年10期
页码:
78-82
栏目:
智能、算法、系统工程
出版日期:
2017-10-10

文章信息/Info

Title:
 A Least Square Linear Classifier with Standard Error
文章编号:
1673-629X（2017)10-0078-05
作者:
 王刚刚[1]; 赵礼峰[1]; 谢亚利[2]
1. 南京邮电大学 理学院；2.上海师范大学 数理学院
Author(s):
 WANG Gang-gang[1] ；ZHAO Li-feng[1]；XIE Ya-li[2]
关键词:
 ; K-means聚类分析; 最小二乘法; 标准误差; 分类器
Keywords:
 ; K-means clustering analysis; least square method; standard error; classifier
分类号:
TP181
文献标志码:
A
摘要:
 大数据时代下数据结构的多样性严重影响人们对数据分类的判断.有效解决数据分类问题并提高分类准确率是大数据时代背景下亟待解决的难题.分类问题是将数据按照某种特征进行划分,并根据分类结果的准确性来判断分类特征的优劣.现有的模式识别中处理无监督分类问题的方法都有着自身固有缺陷.人为主观选择分类特征会降低模型的拟合效果.为此,提出一种将标准误差作为分类特征的线性分类器.该分类器在对样本进行分类的过程中,可保证分类的标准误差最小,从而保证了模型最终分类结果的准确性最高.基于该分类器进行了建模仿真验证.仿真实验结果表明,该分类器对样本分类的标准误差较小,准确率较高且复杂度也相对较低.相对于其他线性分类器,该分类器具有高效性和实效性的优势.
Abstract:
 The diversity of the data structure in the era of big data can seriously affect the people’ s judgment of the data classification, which will be an urgent difficulty to solve data classification effectively and improve the accuracy of classification under the background of big data. Classification is to classify the data according to some characteristics and to judge the merits of classification characteristics by the accuracy of the classification results. The methods dealing with unsupervised learning classification in existing pattern recognition have their own inherent defects. Artificial subjective selection of classification characteristics will reduce the model fitting effect. Therefore,a linear classifier is proposed that the standard error is used as the classification feature to classify the data. In the process of classifying sam-ples,it can ensure the minimum standard error of the classification,thus ensuring the highest accuracy of the final classification results. The simulation shows that it has less standard error,higher accuracy and lower complexity. Compared with other linear classifiers,it has the advantages of high efficiency and effectiveness.

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更新日期/Last Update: 2017-11-23