Learning One More Thing
Abstract
Most research on machine learning has focused on scenarios in which a learner faces a single, isolated learning task. The lifelong learning framework assumes instead that the learner encounters a multitude of related learning tasks over its lifetime, providing the opportunity for the transfer of knowledge. This paper studies lifelong learning in the context of binary classification. It presents the invariance approach, in which knowledge is transferred via a learned model of the invariances of the domain. Results on learning to recognize objects from color images demonstrate superior generalization capabilities if invariances are learned and used to bias subsequent learning.
Cite
Text
Thrun and Mitchell. "Learning One More Thing." International Joint Conference on Artificial Intelligence, 1995. doi:10.21236/ada285342Markdown
[Thrun and Mitchell. "Learning One More Thing." International Joint Conference on Artificial Intelligence, 1995.](https://mlanthology.org/ijcai/1995/thrun1995ijcai-learning/) doi:10.21236/ada285342BibTeX
@inproceedings{thrun1995ijcai-learning,
title = {{Learning One More Thing}},
author = {Thrun, Sebastian and Mitchell, Tom M.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1995},
pages = {1217-1225},
doi = {10.21236/ada285342},
url = {https://mlanthology.org/ijcai/1995/thrun1995ijcai-learning/}
}