Process-Oriented Estimation of Generalization Error
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 714-720.
Abstract
Methods to avoid overfitting fall into two broad categories: data-oriented (using separate data for validation) and representation-oriented (penalizing complexity in the model) . Both have limitations that are hard to overcome. We argue that fully adequate model evaluation is only possible if the search process by which models are obtained is also taken into account. To this end, we recently proposed a method for process-oriented evaluation (POE), and successfully applied it to rule induction [Domingos, 1998b]. However, for the sake of simplicity this treatment made a number of rather artificial assumptions. In this paper the assumptions are removed, and a simple formula for error estimation is obtained. Empirical trials show the new, better-founded form of POE to be as accurate as the previous one, while further reducing theory sizes.
@inproceedings{ijcai1999p102,
title = {Process-Oriented Estimation of Generalization Error},
author = {Domingos, Pedro M.},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {714--720},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/102},
}
TY - CPAPER TI - Process-Oriented Estimation of Generalization Error AU - Domingos, Pedro M. T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 714 EP - 720 UR - https://ijcai.org/proceedings/1999/102 ER -Copied
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