A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Text categorization by boosting automatically extracted concepts
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using Kullback-Leibler distance for text categorization
ECIR'03 Proceedings of the 25th European conference on IR research
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Nowadays most of text categorization algorithms use vector space model. It can not make full use of position of terms, and the position brings much semantic information. This paper proposes a coordinate model. By using this model, the terms' position information can be utilized. In this model, some central terms are selected as origin and a multidimensional space is built, other words will be put into this space by their position relative to these origin. In our experiment, we present a boost algorithm based on coordinate model. The result shows that there are much information can be mined from coordinate model.