|
eCommons@Cornell >
Faculty of Computing and Information Science >
Computing and Information Science >
Computing and Information Science Technical Reports >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1813/11580
| Title: | Graphical Multi-Task Learning |
| Authors: | Sheldon, Daniel |
| Keywords: | Multi-Task Learning
Networks |
| Issue Date: | 31-Oct-2008 |
| Abstract: | We investigate the problem of learning multiple tasks that are related according to a network structure, using the multi-task kernel framework proposed by Evgeniou, Micchelli and Pontil. Our method combines a graphical task kernel with an arbitrary base kernel.We demonstrate its effectiveness on a real ecological application that inspired this work. |
| URI: | http://hdl.handle.net/1813/11580 |
| Appears in Collections: | Computing and Information Science Technical Reports
|
Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.
|
