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Learning vs. Generalization

  • Noise in the actual data is never a good thing, since it limits the accuracy of generalization that can be achieved no matter how extensive the training set is.
  • Non-perfect learning is better in this case!
        • „Perfect“ learning achieves the dotted separation, while the desired one is in fact given by the solid line.
  • However, injecting artificial noise (so-called jitter ) into the inputs during training is one of several ways to improve generalization
  • „Perfect“ learning achieves the dotted separation, while the desired one is in fact given by the solid line.

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