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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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