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Neural Network Exploration Using Optimal Experiment Design

Author(s)
Cohn, David A.
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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 has much to offer, especially in domains where its high computational costs can be tolerated.
Date issued
1994-06-01
URI
http://hdl.handle.net/1721.1/6631
Other identifiers
AIM-1491
Series/Report no.
AIM-1491

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  • AI Memos (1959 - 2004)

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