Boosted multi-task learning |
| |
Authors: | Olivier Chapelle Pannagadatta Shivaswamy Srinivas Vadrevu Kilian Weinberger Ya Zhang Belle Tseng |
| |
Affiliation: | 1. Yahoo! Labs, Sunnyvale, CA, USA 2. Department of Computer Science, Cornell University, Ithaca, NY, USA 3. Washington University, Saint Louis, MO, USA 4. Shanghai Jiao Tong University, Shanghai, China
|
| |
Abstract: | In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between ? 1-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|