Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2011.94
Title: Incorporating prior-knowledge in support vector machines by kernel adaptation
Authors: Veillard, A.
Racoceanu, D.
Bressan, S. 
Keywords: Breast cancer
Kernel
Prior-knowledge
Support vector machine
Issue Date: 2011
Source: 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. https://doi.org/10.1109/ICTAI.2011.94
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
URI: http://scholarbank.nus.edu.sg/handle/10635/40263
ISBN: 9780769545967
ISSN: 10823409
DOI: 10.1109/ICTAI.2011.94
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