Abstract
One aim of Meta-learning techniques is to minimize the time needed for problem solving, and the effort of parameter hand-tuning, by automating algorithm selection. The predictive model of algorithm performance needed for task often requires long training times. We address the problem in an online fashion, running multiple algorithms in parallel on a sequence of tasks, continually updating their relative priorities according to a neural model that maps their current state to the expected time to the solution. The model itself is updated at the end of each task, based on the actual performance of each algorithm. Censored sampling allows us to train the model effectively, without need of additional exploration after each task’s solution. We present a preliminary experiment in which this new inter-problem technique learns to outperform a previously proposed intra-problem heuristic.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Gagliolo, M., Schmidhuber, J. (2005). A Neural Network Model for Inter-problem Adaptive Online Time Allocation. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_2
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DOI: https://doi.org/10.1007/11550907_2
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