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Abstract: Traditional Meta-Learning requires long training times, and is often focused on optimizing performance quality, neglecting computational complexity. Algorithm Portfolios are more robust, but present similar limitations. We reformulate algorithm selection as a time allocation problem: all candidate algorithms are run in parallel, and their relative priorities are continually updated based on runtime information, with the aim of minimizing the time to reach a desired performance level. Each... (Update)
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BibTeX entry: (Update)
@unpublished{ gagliolo06dynamic,
author = {Matteo Gagliolo and J\"{u}rgen Schmidhuber},
title = {Dynamic Algorithm Portfolios},
year = {2006},
month = {January},
note = {Ninth International Symposium on Artificial Intelligence and Mathematics},
url = {citeseer.ist.psu.edu/731557.html} }
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1 A neural network model for inter-problem adaptive online tim.. - Gagliolo, Schmidhuber - 2005
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