Please use this identifier to cite or link to this item:
Title: Incorporating prior-knowledge in support vector machines by kernel adaptation
Authors: Veillard, A.
Racoceanu, D.
Bressan, S. 
Keywords: Breast cancer
Support vector machine
Issue Date: 2011
Citation: Veillard, A., Racoceanu, D., Bressan, S. (2011). Incorporating prior-knowledge in support vector machines by kernel adaptation. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI : 591-596. ScholarBank@NUS Repository.
Abstract: SVMs with the general purpose RBF kernel are widely considered as state-of-the-art supervised learning algorithms due to their effectiveness and versatility. However, in practice, SVMs often require more training data than readily available. Prior-knowledge may be available to compensate this shortcoming provided such knowledge can be effectively passed on to SVMs. In this paper, we propose a method for the incorporation of prior-knowledge via an adaptation of the standard RBF kernel. Our practical and computationally simple approach allows prior-knowledge in a variety of forms ranging from regions of the input space as crisp or fuzzy sets to pseudo-periodicity. We show that this method is effective and that the amount of required training data can be largely decreased, opening the way for new usages of SVMs. We propose a validation of our approach for pattern recognition and classification tasks with publicly available datasets in different application domains. © 2011 IEEE.
Source Title: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISBN: 9780769545967
ISSN: 10823409
DOI: 10.1109/ICTAI.2011.94
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 15, 2018


checked on Oct 8, 2018

Page view(s)

checked on Oct 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.