Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
The nature of statistical learning theory
The nature of statistical learning theory
WordNet: a lexical database for English
Communications of the ACM
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
CONSTRUE/TIS: A System for Content-Based Indexing of a Database of News Stories
IAAI '90 Proceedings of the The Second Conference on Innovative Applications of Artificial Intelligence
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Web page feature selection and classification using neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Twin least squares support vector regression
Neurocomputing
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As a machine intelligence paradigm, the support vector machines (SVMs) have tremendous potential for helping people to classify text document into a fixed number of predefined categories. The purpose of this paper is to discuss a new method of feature selection combined with principal component analysis and class profile-based feature as an input vector for SVMs classifier, and to demonstrate the effectiveness of this process. This paper also demonstrates that an applied method with SVMs improves categorisation performance and reduces the amount of time required to configure a learning machine.