I want to use a neural network to predict the refractive index of a solution. My thinking is, instead of immediately training on many samples, I will first find the 'ultimate resolution' of the network given the experimental apparatus I am using. What I mean is I will make two different solutions which have refractive indices near the middle of the range of which I am interested. Then I will train the network to classify these two solutions based on reflectance measured from the solution. If it works, say with at least 95% accuracy, then I will make two different solutions in which the difference in refractive index is smaller than before. I will repeat this until the ANN classifies, say below 95%.

Will this method of finding the 'resolution' by classification extrapolate well to regression with many more training examples?

  • $\begingroup$ What is a "refractive index of a solution"? Are you talking about the refractive index of a material? $\endgroup$
    – nbro
    Jul 12 '20 at 13:23
  • $\begingroup$ I am measuring the refractive index of a solution based on reflectance data. I'm confused why your confused, do you need me to clarify what a solution is? $\endgroup$
    – pmac
    Jul 13 '20 at 0:23
  • $\begingroup$ Well, not everyone may be familiar with your terminology, but, nevertheless, could help, so it may be a good idea to explain certain terms. $\endgroup$
    – nbro
    Jul 13 '20 at 10:52
  • $\begingroup$ Solution is a solute dissolved in a solvent. Reflectance meaning I will put the solution in the apparatus and shine light on it and measure the reflected light. $\endgroup$
    – pmac
    Jul 13 '20 at 14:09

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