Abstract
This paper aims to improve the response performance of min-max modular classifier by a module selection policy for two-class classification during recognition. We propose an efficient base classifier selection algorithm. We show that the quadratic complexity of original min-max modular classifier can fall onto the level of linear complexity in the number of base-classifier modules for each input sample under presented selection scheme. The experimental results indicate the effectiveness of our algorithm and verify our theoretical analysis.
This work was supported in part by the National Natural Science Foundation of China via the grants NSFC 60375022 and NSFC 60473040.
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Zhao, H., Lu, B. (2005). Improvement on Response Performance of Min-Max Modular Classifier by Symmetric Module Selection. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_7
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DOI: https://doi.org/10.1007/11427445_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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