Recognition of handwritten Chinese characters by critical region analysis
Pattern Recognition
Combination of feature selection approaches with SVM in credit scoring
Expert Systems with Applications: An International Journal
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Handwritten Chinese character recognition is difficult due to the unstructured and noisy nature of its training examples. There are often too few training examples for a statistical learner like SVM to overcome the noise and extract useful information reliably. Existing prior domain knowledge represents a valuable source of information for classifying handwritten characters. Explanation-based learning (EBL) provides a way to incorporating prior domain knowledge into the learner. The dynamic bias formed by the interaction of domain knowledge with training examples can yield solution knowledge of potential higher quality. Two EBL approaches, one that uses a special feature kernel function in SVM, the other uses a conventional kernel for the SVM but provides additional preference in choosing the classification hyperplane, are reported.