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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 50, Iss. 22 — Aug. 1, 2011
  • pp: PDM1–PDM2

Practical data mining and machine learning for optics applications: introduction to the feature issue

Ghaleb Abdulla, Abdul Awwal, Kirk Borne, Tin Kam Ho, and W. Thomas Vestrand  »View Author Affiliations


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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