DocumentCode
2552202
Title
Text categorization algorithms representations based on inductive learning
Author
Jian-Fang, Cao ; Hong-bin, Wang
Author_Institution
Dept. of Comput. Sci., Xinzhou Teachers Univ., Xinzhou, China
fYear
2010
fDate
16-18 April 2010
Firstpage
352
Lastpage
355
Abstract
Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks. Categorization algorithm is the most critical factor to text categorization system performance. The inductive learning classifiers are put forward. Very accurate text categorization result can be learned automatically from training examples.
Keywords
learning by example; natural language processing; pattern classification; text analysis; inductive learning classifiers; information organization; management tasks; natural language text assignment; text categorization algorithm representation; Classification tree analysis; Content management; Information filtering; Information filters; Information management; Learning systems; Machine learning; Natural languages; Testing; Text categorization; classification; inductive learnin; text categorization;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5263-7
Electronic_ISBN
978-1-4244-5265-1
Type
conf
DOI
10.1109/ICIME.2010.5477992
Filename
5477992
Link To Document