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Abstract: We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action... (Update)
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BibTeX entry: (Update)
Cohn, DA (1994). Neural network exploration using optimal experiment design. In JD Cowan, G Tesauro & J Allspector, editors, Advances in Neural Information Processing Systems, 6. San Mateo, CA: Morgan Kaufmann, 679-686. http://citeseer.ist.psu.edu/article/cohn94neural.html More
@inproceedings{ cohn94neural,
author = "David A. Cohn",
title = "Neural Network Exploration Using Optimal Experiment Design",
booktitle = "Advances in Neural Information Processing Systems",
volume = "6",
publisher = "Morgan Kaufmann Publishers, Inc.",
editor = "Jack D. Cowan and Gerald Tesauro and Joshua Alspector",
pages = "679--686",
year = "1994",
url = "citeseer.ist.psu.edu/article/cohn94neural.html" }
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