Encyclopedia > Early stopping

  Article Content

Early stopping

Early stopping is a form of regularization[?] used when a machine learning model (such as a neural network) is trained by on-line gradient descent. In early stopping, the training set is split into a new training set and a validation set. Gradient descent is applied to the new training set. After each sweep through the new training set, the network is evaluated on the validation set. The network with the best performance on the validation set is then used for actual testing.

Early stopping is very common practice in neural network training and often produces networks that generalize well.



All Wikipedia text is available under the terms of the GNU Free Documentation License

 
  Search Encyclopedia

Search over one million articles, find something about almost anything!
 
 
  
  Featured Article
Canadian Music Hall of Fame

... Clayton-Thomas[?] 1996 Denny Doherty[?] 1996 John Kay[?] 1996 Dominic Troiano[?] 1996 Zal Yanovsky 1997 Gil Evans[?] 1997 Lenny Breau[?] 1997 Maynard ...

 
 
 
This page was created in 36.1 ms