Bayesian online classifiers for text classification and filtering

  • Authors:
  • Kian Ming Adam Chai;Hai Leong Chieu;Hwee Tou Ng

  • Affiliations:
  • DSO National Laboratories, Singapore;DSO National Laboratories, Singapore;National University of Singapore, Singapore

  • Venue:
  • SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

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.