Boosting SVM classifiers by ensemble

By far, the support vector machines (SVM) achieve the state-of-theart 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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; I.5.2 [Pattern Recognition]: Design Methodology General Terms Algorithms, Experimentation Keywords Classifier design and evaluation, Information filtering, Machine learning, Neural nets, Text processing

• Type: Conference
• Year: 2005
• Where: WWW
• Authors: Yan-Shi Dong, Ke-Song Han
• Keywords: Internet Technology, Keywords Classifier Design, Strong Classifiers, Weak Classifiers, WWW 2005