A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Journal of Intelligent Information Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time web text classification and analysis of reading difficulty
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Text relatedness based on a word thesaurus
Journal of Artificial Intelligence Research
Constructing sparse KFDA using pre-image reconstruction
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
A soft real-time web news classification system with double control loops
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Semantic smoothing for text clustering
Knowledge-Based Systems
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A number of powerful kernel-based learning machines, such as support vector machines (SVMs), kernel Fisher discriminant analysis (KFDA), have been proposed with competitive performance. However, directly applying existing attractive kernel approaches to text classification (TC) task will suffer semantic related information deficiency and incur huge computation costs hindering their practical use in numerous large scale and real-time applications with fast testing requirement. To tackle this problem, this paper proposes a novel semantic kernel-based framework for efficient TC which offers a sparse representation of the final optimal prediction function while preserving the semantic related information in kernel approximate subspace. Experiments on 20-Newsgroup dataset demonstrate the proposed method compared with SVM and KNN (K-nearest neighbor) can significantly reduce the computation costs in predicating phase while maintaining considerable classification accuracy.