Early stopping is a very common practice in neural network training and often produces networks that generalize well. However, while often improving the generalization it does not do so in a mathematically well-defined way.
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Early stopping
One of the simplest and most widely used means of avoiding overfitting is to divide the data into two sets: a training set and a validation set. We train using only the training data.
One of the solutions is early stopping (Sarle, 1995), but this approach need more critical intention as this problem is harder than expected (Lawrence et al., 1997).
Statistical learning theory is concerned with training classifiers on a limited amount of data. In the context of neural networks a simple heuristic, called early stopping, ...
This particular example has to do with overfitting the model - in this case fitting the model too closely to the idiosyncrasies of the training data. This can be fixed later on but clearly stopping the building of the tree short of either one record ...
 See also: Machine learning, Neural network, Overfitting, Regression, Knowledge
  
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