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ICANN

2005

Springer

2005

Springer

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...

Related Content

Added |
27 Jun 2010 |

Updated |
27 Jun 2010 |

Type |
Conference |

Year |
2005 |

Where |
ICANN |

Authors |
Matteo Gagliolo, Jürgen Schmidhuber |

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