Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Subset Selection in Text-Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Evaluation and Construction of Training Corpuses for Text Classification: A Preliminary Study
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
An Approach to Improve Text Classification Efficiency
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
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This paper reports a system that hierarchically classifies Chinese web documents without dictionary support and segmentation procedure. In our classifier, Web documents are represented by N-grams (N≤4) that are easy to be extracted. A boosting machine learning approach is applied to classifying Web Chinese documents that share a topic hierarchy. The open and modularized system architecture makes our classifier be extendible. Experimental results show that our system can effectively and efficiently classify Chinese Web documents.