Suppose I want to do reservoir computing to classify the input to the proper category (e.g. recognizing a handwritten letter).

Ideally, after training a single reservoir and testing it, there would be an output vector y with one value close to 1 and the others close to 0. However, this is not the case in practice, and I don't want to make the reservoir bigger at the moment.

I was therefore thinking of combining the predictions of a number of independent reservoirs for higher precision. After all, each reservoir does not only contain information on the 'most probable' output, but also on the 'second most probable' etc. I have by now found out that renormalizing the output from each reservoir to a probability can be done with the 'softmax' function. But how to combine the probabilities assigned by the individual reservoirs to a total probability, with the hope that combining enough independent reservoirs would lead to a single element of the total y that is a clear outlier?

Some ways that I thought of:

  • Multiply the probabilities obtained through the different reservoirs (this seems the most intuitive to me)
  • Take the average
  • For each letter that has to be classified, just pick the reservoir that assigns the highest probability to one of the possibilities; or where the difference between first and second most probable possibility is largest

Are such schemes appropriate? Or should I do something else? I saw a paper where they trained a second layer to combine inputs from different reservoir, but this seems too far-fetched at the moment, something simple would be preferable.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.