A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms, decision thresholds and decision boundaries. Based on the center sliding Bayesian framework, a novel orthogonal polynomial classifier is subsequently developed. The orthogonal polynomial classifier is evaluated using two representative orthogonal polynomials for feature mapping. Our experimental results show promising potential of the orthogonal polynomial classifier since it achieves both desired accuracy and computational efficiency.
Bibliographical noteFunding Information:
The authors would like to thank the anonymous reviewers for their constructive suggestions. Lei Sun is also thankful to Professor Dapeng (Oliver) Wu from University of Florida for hosting his visit during 2011–2012. His kind technical assistance and advice has largely formed the background foundation for this paper. This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology ( NRF-2012R1A1A2042428 ).
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence