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ICANN
2005
Springer

A Neural Network Model for Inter-problem Adaptive Online Time Allocation

10 years 9 months ago
A Neural Network Model for Inter-problem Adaptive Online Time Allocation
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 this 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. 1 Problem statement A typical machine learning scenario involves a (possibly inexperienced) practition...
Matteo Gagliolo, Jürgen Schmidhuber
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICANN
Authors Matteo Gagliolo, Jürgen Schmidhuber
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