Machine Learning
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Feature Kernel Functions: Improving SVMs Using High-Level Knowledge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
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Existing prior domain knowledge represents a valuable source of information for image interpretation problems such as classifying handwritten characters. Such domain knowledge must be translated into a form understandable by the learner. Translation can be realized with Explanation-Based Learning (EBL) which provides a kind of dynamic inductive bias, combining domain knowledge and training examples. The dynamic bias formed by the interaction of domain knowledge with training examples can yield solution knowledge of potential higher quality than can be anticipated by the static bias designer without seeing training examples. We detail how EBL can be used to dynamically integrate domain knowledge, training examples, and the learning mechanism, and describe the two EBL approaches in (Sun & DeJong 2005a) and (Sun & DeJong 2005b).