Electron microscopy
  
Error Excess
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Error excess is given by,

          Error excess ------------------------- [3761a]

Where,

          Approximation excess is related to overfitting in machine learning and can be seen as the extra amount of complexity or flexibility in a model beyond what is necessary to represent the underlying data distribution..

          Estimation error refers to the difference between the predicted or estimated value and the true value of the quantity we are trying to predict (see page3765).

If R* = εBayes on both sides of Equation 3761a, then we can get,

          Error excess ------------------------- [3761b]

 

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