The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Information Processing and Management: an International Journal
Projections for efficient document clustering
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Making large-scale support vector machine learning practical
Advances in kernel methods
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
An empirical study of required dimensionality for large-scale latent semantic indexing applications
Proceedings of the 17th ACM conference on Information and knowledge management
Recursive Bayesian linear regression for adaptive classification
IEEE Transactions on Signal Processing
Orientation distance-based discriminative feature extraction for multi-class classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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Latent Semantic Indexing (LSI) is an effective method to extract features that captures underlying latent semantic structure in the word usage across documents, However, subspace selected by this method may not be the most appropriate one to classify documents, since it orders extracted features according to their variances, not the classification power. We propose to apply feature ordering method based on support vector machines in order to select LSI-features that is suited for classification. Experimental results suggest that the method improves classification performance with considerably more compact representation.