Improving the Accuracy of Least-Squares Probabilistic Classifiers
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概要
- 論文の詳細を見る
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 classifiers 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.
- 2011-06-01
著者
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Wichern Gordon
Mit Lincoln Laboratory
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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SIMM JAAK
Tokyo Institute of Technology
-
Simm Jaak
Department Of Computer Science Tokyo Institute Of Technology
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YAMADA Makoto
Tokyo Institute of Technology
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Yamada Makoto
Tokyo Inst. Of Technol.
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Wichern Gordon
Arizona State University
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