Abstract
Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.
The related work is supported by the Key Project of Chinese Ministry of Education (No.105150) and the Foundation of ATR Key Lab (51483010305DZ0207). Thanks to Prof. H. Ogawa of Tokyo Institute of Technology for helpful discussions.
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Liu, B., Zhang, J., Chen, X. (2007). Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_46
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DOI: https://doi.org/10.1007/978-3-540-72393-6_46
Publisher Name: Springer, Berlin, Heidelberg
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