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IJCNN
2007
IEEE

Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent

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Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent
—The 0/1 loss is an important cost function for perceptrons. Nevertheless it cannot be easily minimized by most existing perceptron learning algorithms. In this paper, we propose a family of random coordinate descent algorithms to directly minimize the 0/1 loss for perceptrons, and prove their convergence. Our algorithms are computationally efficient, and usually achieve the lowest 0/1 loss compared with other algorithms. Such advantages make them favorable for nonseparable real-world problems. Experiments show that our algorithms are especially useful for ensemble learning, and could achieve the lowest test error for many complex data sets when coupled with AdaBoost.
Ling Li, Hsuan-Tien Lin
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IJCNN
Authors Ling Li, Hsuan-Tien Lin
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