https://scholars.lib.ntu.edu.tw/handle/123456789/330990
標題: | Optimizing 0/1 loss for perceptrons by random coordinate descent | 作者: | Li, L. HSUAN-TIEN LIN |
公開日期: | 2007 | 起(迄)頁: | 749-754 | 來源出版物: | IEEE International Conference on Neural Networks | 摘要: | 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. ©2007 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-41549092416&doi=10.1109%2fIJCNN.2007.4371051&partnerID=40&md5=0b5128bea409c6b1b7657730f5ed5057 http://scholars.lib.ntu.edu.tw/handle/123456789/330990 |
DOI: | 10.1109/IJCNN.2007.4371051 | SDG/關鍵字: | Adaptive boosting; Complex datasets; Computationally efficient; Coordinate descent; Ensemble learning; Nonseparable; Perceptron learning; Real-world problem; Test errors; Cost functions |
顯示於: | 資訊工程學系 |
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