Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings. We further present a general framework that integrates the above measurements and embeddings with existing transfer learning algorithms for higher performance. It has demonstrated to be robust in two real-world applications: bus delay forecasting and air quality forecasting.
|Title of host publication||Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI2019)|
|Publisher||AAAI Press/International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 1 Aug 2019|
|Event||Twenty-Eighth International Joint Conference on Artificial Intelligence - Macao, China|
Duration: 10 Aug 2019 → 16 Aug 2019
|Conference||Twenty-Eighth International Joint Conference on Artificial Intelligence|
|Abbreviated title||IJCAI 2019|
|Period||10/08/19 → 16/08/19|
This work is partially funded by NSFC91846204.