A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Is Combining Classifiers Better than Selecting the Best One
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Using Ensemble-Based Reasoning to Help Experts in Melanoma Diagnosis
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Secure Cover Selection Steganography
ISA '09 Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance
SVM Based Decision Analysis and Its Granular-Based Solving
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
BSS: Boosted steganography scheme with cover image preprocessing
Expert Systems with Applications: An International Journal
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By far, the support vector machines (SVM) achieve the state-of-the-art performance for the text classification (TC) tasks. Due to the complexity of the TC problems, it becomes a challenge to systematically develop classifiers with better performance. We try to attack this problem by ensemble methods, which are often used for boosting weak classifiers, such as decision tree, neural networks, etc., and whether they are effective for strong classifiers is not clear.