TY - GEN
T1 - Incorporating prior knowledge with weighted margin support vector machines
AU - Wu, Xiaoyun
AU - Srihari, Rohini
PY - 2004
Y1 - 2004
N2 - Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus a larger training dataset is required for better performance. In some applications, there might be human knowledge available that, in principle, could compensate for the lack of data. In this paper, we propose a simple generalization of SVM: Weighted Margin SVM (WMSVMs) that permits the incorporation of prior knowledge. We show that Sequential Minimal Optimization can be used in training WMSVM. We discuss the issues of incorporating prior knowledge using this rather general formulation. The experimental results show that the proposed methods of incorporating prior knowledge is effective.
AB - Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus a larger training dataset is required for better performance. In some applications, there might be human knowledge available that, in principle, could compensate for the lack of data. In this paper, we propose a simple generalization of SVM: Weighted Margin SVM (WMSVMs) that permits the incorporation of prior knowledge. We show that Sequential Minimal Optimization can be used in training WMSVM. We discuss the issues of incorporating prior knowledge using this rather general formulation. The experimental results show that the proposed methods of incorporating prior knowledge is effective.
KW - Incorporating Prior Knowledge
KW - Support Vector Machines
KW - Text Categorization
UR - https://www.scopus.com/pages/publications/12244303260
U2 - 10.1145/1014052.1014089
DO - 10.1145/1014052.1014089
M3 - Conference contribution
AN - SCOPUS:12244303260
SN - 1581138881
SN - 9781581138887
T3 - KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 326
EP - 333
BT - KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 22 August 2004 through 25 August 2004
ER -