Sample-based regularization for support vector machine classification
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017 |
| Publisher | IEEE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538618417 |
| DOIs | |
| Publication status | Published - 8 Mar 2018 |
| Publication type | A4 Article in a conference publication |
| Event | International Conference on Image Processing Theory, Tools and Applications - Montreal, Canada Duration: 28 Nov 2017 → 1 Dec 2017 |
Conference
| Conference | International Conference on Image Processing Theory, Tools and Applications |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 28/11/17 → 1/12/17 |
Abstract
In this paper, we propose a new regularization scheme for the well-known Support Vector Machine (SVM) classifier that operates on the training sample level. The proposed approach is motivated by the fact that Maximum Margin-based classification defines decision functions as a linear combination of the selected training data and, thus, the variations on training sample selection directly affect generalization performance. We show that the exploitation of the proposed regularization scheme is well motivated and intuitive. Experimental results show that the proposed regularization scheme outperforms standard SVM in human action recognition tasks as well as classical recognition problems.
ASJC Scopus subject areas
Keywords
- Dropout, kernel methods, Regularization, Support Vector Machine