3 Matching Annotations
  1. Jul 2018
  2. Feb 2017
    1. SVM only cares that the difference is at least 10

      The margin seems to be manually set by the creator in the loss function. In the sample code, the margin is 1-- so the incorrect class has to be scored lower than the correct class by 1.

      How is this margin determined? It seems like one would have to know the magnitude of the scores beforehand.

      Diving deeper, is the scoring magnitude always the same if the parameters are normalized by their average and scaled to be between 0 and 1? (or -1 and -1... not sure of the correct scaling implementation)

      Coming back to the topic -- is this 'minimum margin' or delta a tune-able parameter?

      What effects do we see on the model by adjusting this parameter?

      What are best and worst case scenarios of playing with this parameter?

  3. Aug 2016
    1. It is a different thing, a set of education policies, investment decisions, and IT practices that actively create and maintain class boundaries through strictures that discriminate against specific groups.

      Highlighted this in response to Jeremy's comment above about filters. Institutional practices and policies are the focus here as well as the physical infrastructure for IT, available equipment.