Neural Network Exploration Using Optimal Experiment Design
Author(s)Cohn, David A.
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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  and MacKay , 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.