A robust linear programming based boosting algorithm
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | 2005 IEEE Workshop on Machine Learning for Signal Processing |
| Pages | 49-54 |
| Publication status | Published - 2005 |
| Externally published | Yes |
Publication series
| Name | 2005 IEEE Workshop on Machine Learning for Signal Processing |
|---|
Conference
| Title | 2005 IEEE Workshop on Machine Learning for Signal Processing |
|---|---|
| Place | United States |
| City | Mystic, CT |
| Period | 28 - 30 September 2005 |
Link(s)
Abstract
AdaBoost has been successfully used in many signal processing systems for data classification. It has been observed that on highly noisy data AdaBoost leads to overfilling. In this paper, a new regularized boosting algorithm LPnorm2-AdaBoost (LPNA), arising from the close connection between AdaBoost and linear programming, is proposed to mitigate the overfilling problem. In the algorithm, the data distribution skewness is controlled during the learning process to prevent outliers from spoiling decision boundaries by introducing a smooth convex penalty function (l2 norm) into the objective of the minimax problem. A stabilized column generation technique is used to transform the optimization problem into a simple linear programming problem. The effectiveness of the proposed algorithm is demonstrated through experiments on a wide variety of datasets. ©2005 IEEE.
Bibliographic Note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
Citation Format(s)
A robust linear programming based boosting algorithm. / Sun, Yijun; Todorovic, Sinisa; Li, Jian et al.
2005 IEEE Workshop on Machine Learning for Signal Processing. 2005. p. 49-54 1532873 (2005 IEEE Workshop on Machine Learning for Signal Processing).
2005 IEEE Workshop on Machine Learning for Signal Processing. 2005. p. 49-54 1532873 (2005 IEEE Workshop on Machine Learning for Signal Processing).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
