Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
The State of the Art in Text Filtering
User Modeling and User-Adapted Interaction
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
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Predicting library of congress classifications from library of congress subject headings
Journal of the American Society for Information Science and Technology
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Personalized classification refers to allowing users to define their own categories and automating the assignment of documents to these categories. In this paper, we examine the use of keywords to define personalized categories and propose the use of Support Vector Machine (SVM) to perform personalized classification. Two scenarios have been investigated. The first assumes that the personalized categories are defined in a flat category space. The second assumes that each personalized category is defined within a pre-defined general category that provides a more specific context for the personalized category. The training documents for personalized categories are obtained from a training document pool using a search engine and a set of keywords. Our experiments have delivered better classification results using the second scenario. We also conclude that the number of keywords used can be very small and increasing them does not always lead to better classification performance.