Generalizing the Bias Term of Support Vector Machines
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 919-923.
Abstract
Based on the study of a generalized form of representer theorem and a specific trick in constructing kernels, a generic learning model is proposed and applied to support vector machines. An algorithm is obtained which naturally generalizes the bias term of SVM. Unlike the solution of standard SVM which consists of a linear expansion of kernel functions and a bias term, the generalized algorithm maps predefined features onto a Hilbert space as well and takes them into special consideration by leaving part of the space unregularized when seeking a solution in the space. Empirical evaluations have confirmed the effectiveness from the generalization in classification tasks.
@inproceedings{ijcai2007p148,
title = {Generalizing the Bias Term of Support Vector Machines},
author = {Li, Wenye and Leung, Kwong-Sak and Lee, Kin-Hong},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {919--923},
year = {2007},
url = {https://ijcai.org/proceedings/2007/148},
}
TY - CPAPER TI - Generalizing the Bias Term of Support Vector Machines AU - Li, Wenye AU - Leung, Kwong-Sak AU - Lee, Kin-Hong T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 919 EP - 923 UR - https://ijcai.org/proceedings/2007/148 ER -Copied
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