Boosting algorithm is understood as the gradient descent algorithm of a loss function. It is often pointed out that the typical boosting algorithm, Adaboost, is seriously affected by the outliers. In this paper, loss functions for robust boosting are studied. Based on a concept of the robust statistics, we propose a positive-part-truncation of the loss function which makes the boosting algorithm robust against extreme outliers. Numerical experiments show that the proposed boosting algorithm is useful for highly noisy data in comparison with other competitors.
|ホスト出版物のタイトル||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|編集者||Nikhil R. Pal, Srimanta Pal, Nikola Kasabov, Rajani K. Mudi, Swapan K. Parui|
|出版ステータス||Published - 2004|
|名前||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)