Transfer Learning Using Task-Level Features with Application to Information Retrieval
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1315-1319.
Abstract
We propose a probabilistic transfer learning model that uses task-level features to control the task mixture selection in a hierarchical Bayesian model. These task-level features, although rarely used in existing approaches, can provide additional information to model complex task distributions and allow effective transfer to new tasks especially when only limited number of data are available. To estimate the model parameters, we develop an empirical Bayes method based on variational approximation techniques. Our experiments on information retrieval show that the proposed model achieves significantly better performance compared with other transfer learning methods.Rong Yan, Jian Zhang
@inproceedings{ijcai2009p221,
title = {Transfer Learning Using Task-Level Features with Application to Information Retrieval},
author = {Yan, Rong and Zhang, Jian},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1315--1319},
year = {2009},
url = {https://ijcai.org/proceedings/2009/221},
}
TY - CPAPER TI - Transfer Learning Using Task-Level Features with Application to Information Retrieval AU - Yan, Rong AU - Zhang, Jian T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1315 EP - 1319 UR - https://ijcai.org/proceedings/2009/221 ER -Copied
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