Boosting SVM classifiers by ensemble
Yan-Shi Dong, Ke-Song Han · 2005
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.