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Matthew E. Taylor. Assisting Transfer-Enabled Machine Learning Algorithms:
Leveraging Human Knowledge for Curriculum Design. In The AAAI 2009 Spring Symposium on Agents that Learn from Human
Teachers, March 2009.
AAAI 2009 Spring Symposium on Agents
that Learn from Human Teachers
Transfer learning is a successful technique that significantlyimproves machine learning algorithms by training on a sequence oftasks rather than a single task in isolation. However, there iscurrently no systematic method for deciding how to construct such asequence of tasks. In this paper, I propose that while humans arewell-suited for the task of curriculum development, significantresearch is still necessary to better understand how to createeffective curricula for machine learning algorithms.
@inproceedings(AAAI09SS-Taylor,
author="Matthew E.\ Taylor",
title="Assisting Transfer-Enabled Machine Learning Algorithms: Leveraging Human Knowledge for Curriculum Design",
Booktitle="The {AAAI} 2009 Spring Symposium on Agents that Learn from Human Teachers",
month="March",
year="2009",
abstract = "Transfer learning is a successful technique that significantly
improves machine learning algorithms by training on a sequence of
tasks rather than a single task in isolation. However, there is
currently no systematic method for deciding how to construct such a
sequence of tasks. In this paper, I propose that while humans are
well-suited for the task of curriculum development, significant
research is still necessary to better understand how to create
effective curricula for machine learning algorithms.",
wwwnote={<a href="http://www.cc.gatech.edu/AAAI-SS09-LFH/Home.html">AAAI 2009 Spring Symposium on Agents that Learn from Human Teachers},
)
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