Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 3,
  • Issue 4,
  • pp. 205-207
  • (2005)

Improving linearity of position-sensitive detector using support vector machines

Not Accessible

Your library or personal account may give you access

Abstract

An intelligent method for improving position linearity of position-sensitive detector (PSD), based on support vector machines (SVMs), is developed. The SVM is established based on the structural risk minimization principle rather than minimizing the empirical error commonly implemented in neural networks. SVM can achieve higher generalization performance. Training SVM is equivalent to solving a linearly constrained quadratic programming problem, thus the solution of SVM is always unique and globally optimal. The improving position linearity procedure has been illustrated using a two-dimensional (2D) PSD. It is pointed out that the position linearity of the measuring system with a proper SVM correction is improved by two orders of magnitude in the measurement range.

© 2005 Chinese Optics Letters

PDF Article
More Like This
Bit-based support vector machine nonlinear detector for millimeter-wave radio-over-fiber mobile fronthaul systems

Yue Cui, Min Zhang, Danshi Wang, Siming Liu, Ze Li, and Gee-Kung Chang
Opt. Express 25(21) 26186-26197 (2017)

Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data

Bin Qi, Chunhui Zhao, Eunseog Youn, and Christian Nansen
Opt. Express 19(27) 26816-26826 (2011)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved