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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Guanqun Cao Ahmad, I. Honglei Zhang Weiyi Xie Gabbouj, M. |
| Copyright Year | 2014 |
| Description | Author affiliation: Tampere Univ. of Technol., Tampere, Finland (Guanqun Cao; Ahmad, I.; Honglei Zhang; Weiyi Xie; Gabbouj, M.) |
| Abstract | We propose a distributed learning to rank method, and demonstrate its effectiveness in web-scale image retrieval. With the increasing amount of data, it is not applicable to train a centralized ranking model for any large scale learning problems. In distributed learning, the discrepancy between the training subsets and the whole when building the models are non-trivial but overlooked in the previous work. In this paper, we firstly include a cost factor to boosting algorithms to balance the individual models toward the whole data. Then, we propose to decompose the original algorithm to multiple layers, and their aggregation forms a superior ranker which can be easily scaled up to billions of images. The extensive experiments show the proposed method outperforms the straightforward aggregation of boosting algorithms. |
| Starting Page | 1422 |
| Ending Page | 1426 |
| File Size | 676378 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780992862619 |
| ISSN | 20761465 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-09-01 |
| Publisher Place | Portugal |
| Access Restriction | Subscribed |
| Rights Holder | EURASIP |
| Subject Keyword | Training Distributed learning Training data Distributed databases Big Data Boosting Data models Big data Learning to rank Bagging |
| Content Type | Text |
| Resource Type | Article |
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