Experimental Design for Bathymetry Editing
eScholarship, University of California, 2020
Online
academicJournal
We describe an application of machine learning to a real-world computerassisted labeling task. Our experimental results expose significant deviationsfrom the IID assumption commonly used in machine learning. These resultssuggest that the common random split of all data into training and testing canoften lead to poor performance.
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Experimental Design for Bathymetry Editing
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Autor/in / Beteiligte Person: | Alafate, Julaiti ; Freund, Yoav ; Sandwell, David T ; Tozer, Brook |
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Veröffentlichung: | eScholarship, University of California, 2020 |
Medientyp: | academicJournal |
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