DocumentCode :
3023893
Title :
Research and Improvement on Bintree Multi-class Categorization Algorithm Based on SVM
Author :
Hu, Yan ; Min, Li ; Xiong, Hao-Yong
Author_Institution :
Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., Wuhan, China
fYear :
2009
fDate :
25-26 April 2009
Firstpage :
582
Lastpage :
585
Abstract :
It´s a hotspot to expend the research on support vector machine from a two-class issue to a multi-class one. Among all kinds of methods, Bintree multi-class text categorization algorithm based on support vector machine is more effective in training and sorting then others, and it works out the impartibility problem. So it is a good method. The dissertation systematically researches and analyses Bintree multi-class text categorization algorithm based on support vector machine, and improves it. That is, assemble first, and then sort them when the size of testing texts is too large. The aim is that after improvement the judgment of the testing text does not have to begin from the base crunode of Bintree, instead the testing text can be put into category function to be computed. The improvement can enhance the efficiency of text categorization and the probability of accurate categorization when the size of testing texts is too big and the quantity of sorted functions is too large.
Keywords :
category theory; sorting; support vector machines; text analysis; trees (mathematics); Bintree multiclass categorization algorithm; Bintree multiclass text categorization algorithm; SVM; base crunode; category function; sorted functions; sorting; support vector machine; testing texts; Algorithm design and analysis; Application software; Blindness; Computer science; Databases; Machine learning algorithms; Sorting; Support vector machines; Testing; Text categorization; Categorization Algorithm; Quadratic Programming; Statistical Learning Theory; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Database Technology and Applications, 2009 First International Workshop on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3604-0
Type :
conf
DOI :
10.1109/DBTA.2009.75
Filename :
5207690
Link To Document :
بازگشت