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|Title:||Incorporating prior-knowledge in support vector machines by kernel adaptation|
Support vector machine
|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. 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|
|Appears in Collections:||Staff Publications|
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