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

Artificial Intelligence and Machine Learning in Optical Information Processing: introduction to the feature issue

Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, and Michael E. Zelinski
Appl. Opt. 61(7) AIML1-AIML1 (2022)

Fiber Optics and Photonics: Emerging Applications—introduction to the feature issue

Satoshi Kawata, Sunil K. Khijwania, Bishnu P. Pal, and H. Y. Tam
Appl. Opt. 50(25) FOP1-FOP1 (2011)

Optics theory and practice in Iberoamerica: introduction to the feature issue

Gustavo Olague, Sambit Bakshi, Josué Álvarez-Borrego, Joseph N. Mait, Amalia Martínez-García, and Markus E. Testorf
Appl. Opt. 59(13) IBO1-IBO5 (2020)

Laser Applications to Chemical, Security, and Environmental Analysis: introduction to the feature issue

Terrence R. Meyer, Volker Ebert, and Wolfgang Schade
Appl. Opt. 50(4) LACSEA1-LACSEA2 (2011)

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)