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IEICE Transactions on Information and Systems
Vol. E94.D (2011) No. 6 P 1337-1340

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http://doi.org/10.1587/transinf.E94.D.1337

Regular Section

The least-squares probabilistic classifier (LSPC) is a computationally-efficient alternative to kernel logistic regression. However, to assure its learned probabilities to be non-negative, LSPC involves a post-processing step of rounding up negative parameters to zero, which can unexpectedly influence classification performance. In order to mitigate this problem, we propose a simple alternative scheme that directly rounds up the classifier's negative outputs, not negative parameters. Through extensive experiments including real-world image classification and audio tagging tasks, we demonstrate that the proposed modification significantly improves classification accuracy, while the computational advantage of the original LSPC remains unchanged.

Copyright © 2011 The Institute of Electronics, Information and Communication Engineers

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