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

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

Feature issue introduction: advances in optical materials

Takunori Taira and Jas Sanghera
Opt. Mater. Express 2(8) 1171-1175 (2012)

Laser applications to chemical, security, and environmental analysis: introduction to the feature issue

Thomas Seeger, Thomas Dreier, Weidong Chen, Sean Kearny, and Waruna Kulatilaka
Appl. Opt. 56(11) LAC1-LAC3 (2017)

Adaptive Optics: introduction to the feature issue

Julian Christou, Brent L. Ellerbroek, Thierry Fusco, and Donald T. Miller
Appl. Opt. 49(31) AO1-AO2 (2010)

Optical pattern recognition algorithms, architecture, and applications: introduction to the feature issue

Abdul A. Awwal, Khan M. Iftekharuddin, Mohammad A. Karim, and Richard D. Juday
Appl. Opt. 42(23) 4647-4648 (2003)

Feature issue introduction: optical ceramics

Jas Sanghera, Takunori Taira, and Hong Jin Kong
Opt. Mater. Express 4(10) 2221-2224 (2014)