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Proceedings Paper

Learning Algorithms In The Infrared
Author(s): Tomas Hirschfeld
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Paper Abstract

The use of learning algorithms to automatically set up analytical procedures in the near IR leads to considerable statistical complexities when one attempts to refine and optimize the method. While current work employs an almost brute force approach using lots of signal/noise ratios, plenty of samples and well defined representative learning sample sets, backed up by plenty of trial and error research, vastly more efficient, more easily automated, and more reliable procedures are, in principle, possible. It soon becomes apparent, however, that the power of our algorithms and the calculating capabilities of our computers are nowhere matched by our understanding of the underlying logic of the procedure. The learning algorithms of correlation spectroscopy, and the optimization process of multilinear regressions underlying them, are logically very complex entities whose complete understanding hinge upon a number of (seemingly) philosophical fine points. While no such complete understanding seems near at present (despite the overwhelming success of our so far crude approach to this spectroscopic technique), a number of insights derived from detailed considerations of the process will be discussed. These include procedures for avoiding optimization pitfalls, overall perform-ance improvements exploiting the non-Gaussian error distribution of the results, simplified optimization routines, and procedures for internal data verification.

Paper Details

Date Published: 20 December 1985
PDF: 4 pages
Proc. SPIE 0553, Fourier and Computerized Infrared Spectroscopy, (20 December 1985); doi: 10.1117/12.970824
Show Author Affiliations
Tomas Hirschfeld, Lawrence Livermore National Laboratory (United States)

Published in SPIE Proceedings Vol. 0553:
Fourier and Computerized Infrared Spectroscopy
David G. Cameron; Jeannette G. Grasselli, Editor(s)

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