Abstract
This paper proposes a method to improve generalization performance of multi-class support vector machines (SVM) based on directed acyclic graph (DAG). At first the structure of DAG is optimized according to training data and Jaakkola-Haussler bound, and then we define fuzzy membership function for each class which is obtained by using average operator in the testing stage and the final recognition result is the class with maximum membership. As a result of our experiment for similar handwritten Chinese characters recognition, the generalization ability of the novel fuzzy multi-class DAG-based SVM classifier is better than that of pair-wise SVM classifier with other combination strategies and its execution time is almost the same as the original DAG.
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© 2005 Springer-Verlag Berlin Heidelberg
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Feng, J., Yang, Y., Fan, J. (2005). Fuzzy Multi-class SVM Classifier Based on Optimal Directed Acyclic Graph Using in Similar Handwritten Chinese Characters Recognition. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_140
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DOI: https://doi.org/10.1007/11427391_140
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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