Efficient Bayesian Task-Level Transfer Learning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2599-2603.
Abstract
In this paper, we show how using the Dirichlet Process mixture model as a generative model of data sets provides a simple and effective method for transfer learning. In particular, we present a hierarchical extension of the classic Naive Bayes classifier that couples multiple Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters and show how recent advances in approximate inference in the Dirichlet Process mixture model enable efficient inference. We evaluate the resulting model in a meeting domain, in which the system decides, based on a learned model of the user's behavior, whether to accept or reject the request on his or her behalf. The extended model outperforms the standard Naive Bayes model by using data from other users to influence its predictions.
@inproceedings{ijcai2007p418,
title = {Efficient Bayesian Task-Level Transfer Learning},
author = {Roy, Daniel M. and Kaelbling, Leslie Pack},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2599--2603},
year = {2007},
url = {https://ijcai.org/proceedings/2007/418},
}
TY - CPAPER TI - Efficient Bayesian Task-Level Transfer Learning AU - Roy, Daniel M. AU - Kaelbling, Leslie Pack T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2599 EP - 2603 UR - https://ijcai.org/proceedings/2007/418 ER -Copied
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