| Article ID: | iaor2005685 |
| Country: | Japan |
| Volume: | 47 |
| Issue: | 3 |
| Start Page Number: | 182 |
| End Page Number: | 196 |
| Publication Date: | Sep 2004 |
| Journal: | Journal of the Operations Research Society of Japan |
| Authors: | Koda Masato, Sano Natsuki, Suzuki Hideo |
| Keywords: | gradient methods, artificial intelligence: decision support, datamining |
We propose a new, robust boosting method by using a sigmoidal function as a loss function. In deriving the method, the stagewise additive modelling methodology is blended with the gradient descent algorithms. Based on intensive numerical experiments, we show that the proposed method is acutally better than AdaBoost and other regularized method in test error rates in the case of noisy, mislabeled situation.