Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Analytical features: a knowledge-based approach to audio feature generation
EURASIP Journal on Audio, Speech, and Music Processing
Explanation-based learning for image understanding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Explanation-based feature construction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Covariance estimation for SAD block matching
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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Kernel functions are often cited as a mechanism to encode prior knowledge of a learning task. But it can be difficult to capture prior knowledge effectively. For example, we know that image pixels of a handwritten character result from a few strokes from a single writing implement; it is not clear how to express this in a kernel function. We investigate an Explanation Based Learning (EBL) paradigm to generate specialized kernel functions. These embody novel high-level features that are automatically constructed from the interaction of prior knowledge and training examples. Our empirical results showed that the performance of the resulting SVM surpasses that of a conventional SVM on the challenging task of classifying handwritten Chinese characters.