Practical data mining and machine learning for optics applications: introduction to the feature issue
Applied Optics, Vol. 50, Issue 22, pp. PDM1-PDM2 (2011)
http://dx.doi.org/10.1364/AO.50.00PDM1
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Abstract
Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.
© 2011 Optical Society of America
OCIS Codes
(000.1200) General : Announcements, awards, news, and organizational activities
(020.1475) Atomic and molecular physics : Bose-Einstein condensates
History
Original Manuscript: July 21, 2011
Manuscript Accepted: July 21, 2011
Published: July 29, 2011
Citation
Ghaleb Abdulla, Abdul Awwal, Kirk Borne, Tin Kam Ho, and W. Thomas Vestrand, "Practical data mining and machine learning for optics applications: introduction to the feature issue," Appl. Opt. 50, PDM1-PDM2 (2011)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-50-22-PDM1
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