Bayesian online classifiers for text classification and filtering
- 11 August 2002
- proceedings article
- Published by Association for Computing Machinery (ACM)
Abstract
This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.Keywords
This publication has 10 references indexed in Scilit:
- A study of thresholding strategies for text categorizationPublished by Association for Computing Machinery (ACM) ,2001
- A re-examination of text categorization methodsPublished by Association for Computing Machinery (ACM) ,1999
- Text categorization with Support Vector Machines: Learning with many relevant featuresPublished by Springer Nature ,1998
- Feature selection, perception learning, and a usability case study for text categorizationPublished by Association for Computing Machinery (ACM) ,1997
- On-line versus Off-line Learning from Random Examples: General ResultsPhysical Review Letters, 1996
- Training algorithms for linear text classifiersPublished by Association for Computing Machinery (ACM) ,1996
- Evaluating and optimizing autonomous text classification systemsPublished by Association for Computing Machinery (ACM) ,1995
- Information-Based Objective Functions for Active Data SelectionNeural Computation, 1992
- Bayesian InterpolationNeural Computation, 1992
- Term-weighting approaches in automatic text retrievalInformation Processing & Management, 1988