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Neural Network Exploration Using Optimal Experiment Design

dc.date.accessioned2004-10-08T20:35:52Z
dc.date.accessioned2018-11-24T10:17:15Z
dc.date.available2004-10-08T20:35:52Z
dc.date.available2018-11-24T10:17:15Z
dc.date.issued1994-06-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6631
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6631
dc.description.abstractWe 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.en_US
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dc.format.extent492706 bytes
dc.language.isoen_US
dc.titleNeural Network Exploration Using Optimal Experiment Designen_US


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