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Feature Kernel Functions: Improving SVMs Using High-Level Knowledge

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Feature Kernel Functions: Improving SVMs Using High-Level Knowledge
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.
Qiang Sun, Gerald DeJong
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Qiang Sun, Gerald DeJong
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