Encode overlapped areas as inputs for Neural Networks (SOM)?

by AqD   Last Updated January 24, 2018 05:19 AM

How am I supposed to represent area input variables to SOM, with the weights/distances to be based on how much they overlap?

I'm trying to encode ranges of numbers. Like:

  • 500-600
  • 550-650
  • 0-100
  • 101-102

Problem is, the underlying calculations of SOM are supposed to use numeric subtraction and multiplication.

So, how do I represent, like: (500-600) is very close to (550-650) (overlap 50 points) while (0-100) has nothing to do with (500-600), same as (0-100) to (101-102)

It's more like boolean toogles of 0...1000, but the distances should really be calculated by OR not subtraction.

How is this usually done? Unless I change the input vector from the original inputs to distances between each of them?

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