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Incremental Bayes Text Categorization Algorithm(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2004年03期
Page:
49-52
Research Field:
Publishing date:

Info

Title:
Incremental Bayes Text Categorization Algorithm
Author(s):
GAO Jie;  JI Genlin
School of Mathematics and Computer Science, Nanjing Normal University, Nanjing 210097, China
Keywords:
text categorization ;  incremental learning ;  NaÇ; ve Bayes
PACS:
TP391.1
DOI:
-
Abstract:
Automatic text categorization is an important research field in data mining and machine learning. An incremental Bayes text categorization algorithm based on small labeled documents is presented to solve the difficult problem involving getting labeled training documents. The algorithm can process two cases : the labeled and unlabeled incremental documents. Directly computing the probability of the samples of a certain class is the processing method for labeled documents. The unlabeled docu ments are labeled first by using the original classification , and then the new classification is trained from the incremental docu ments. The experimental results show that this algorithm is feasible and effective , providing a new method for updating of classi fication.

References:

[1 ] Rish I. An empirical study of the naÇve Bayes classifier[C] . IJCAI - 01 workshop on“Empirical Methods in AI”Technical reports , 2001. 215.
[2 ] SAMUEL KOTZ. Modern Bayesian Statistics [M] . George Washington University Press , 2000. 109.
[3 ] Geiger D , Heckeman D. A characterization of the Dirichlet distribution with applicable to learning Bayesian network[A] . Proceedings of Eleventh Conference on Uncertainty in Artifi cial Intelligence[C] . Montreal , QU ,1995. 196- 207.
[4 ] Dominigos P , Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss [J ] . Machine Learn ing ,1997 ,29 (2 3) :103- 130.
[5 ] Kamal Nigam , et al . Learning to classify the text from la beled and unlabeled documents[A] . Proc 15th National Con ference on Artificial Intelligence [C] . Wisconsin ,1998. 792- 799.

Memo

Memo:
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Last Update: 2013-04-29