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DIS
2007
Springer

Discovering Communicable Models from Earth Science Data

12 years 4 months ago
Discovering Communicable Models from Earth Science Data
Abstract. This chapter describes how we used regression rules to improve upon results previously published in the Earth science literature. In such a scientific application of machine learning, it is crucially important for the learned models to be understandable and communicable. We recount how we selected a learning algorithm to maximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.
Mark Schwabacher, Pat Langley, Christopher Potter,
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where DIS
Authors Mark Schwabacher, Pat Langley, Christopher Potter, Steven A. Klooster, Alicia Torregrosa
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