The nature of statistical learning theory
The nature of statistical learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
A robust minimax approach to classification
The Journal of Machine Learning Research
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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With a great amount of textual information are available on the Internet and corporate intranets, it has become a necessary to categorize large documents. As we known, text classification problem is representative multiclass problem. This paper describes a framework, which we call Strong-to-Weak- to-Strong (SWS). It transforms a “strong” learning algorithm to a “weak” algorithm by decreasing its iterative numbers of optimization while preserving its other characteristics like geometric properties and then makes use of the kernel trick for “weak” algorithms to work in high dimensional spaces, finally improves the performances of text classification. We analyzed the particular properties of learning with text and identified why this approach is appropriate for this task. Empirical results show that our approach is competitive with the other methods.