CHINESE TEXT CATEGORIZATION STUDY BASED ON FEATURE WEIGHT LEARNING

被引：1

作者：

Zhan, Yan ^{[1
]}

Chen, Hao ^{[1
]}

Zhang, Su-Fang ^{[2
]}

Zheng, Mei ^{[3
]}

机构：

[1] Hebei Univ, Coll Math & Comp Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Peoples R China

[2] Hebei Informat Engn Sch, Teaching & Res Sect Math, Baoding 071000, Peoples R China

[3] Yanshan Univ, Coll Int Educ, Qinhuangdao 066004, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Text Categorization; Feature weight; K-NN;

D O I：

10.1109/ICMLC.2009.5212257

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Text Categorization(TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different function of every feature by using feature weight learning. In this paper Text Categorization via K-nearest neighbor algorithm based on feature weight learning is described. The numerical experiments prove the validity of this learning algorithm.

引用

页码：1723 / +

页数：2

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