The general inefficiency of batch training for gradient descent learning
, and .
Neural Networks 16 (10): 1429 - 1451 (2003)

Gradient descent training of neural networks can be done in either a batch or on-line manner. A widely held myth in the neural network community is that batch training is as fast or faster and/or more ‘correct’ than on-line training because it supposedly uses a better approximation of the true gradient for its weight updates. This paper explains why batch training is almost always slower than on-line training—often orders of magnitude slower—especially on large training sets. The main reason is due to the ability of on-line training to follow curves in the error surface throughout each epoch, which allows it to safely use a larger learning rate and thus converge with less iterations through the training data. Empirical results on a large (20,000-instance) speech recognition task and on 26 other learning tasks demonstrate that convergence can be reached significantly faster using on-line training than batch training, with no apparent difference in accuracy.
  • @pl_itwm
  • @thoni
  • @nosebrain
  • @dblp
This publication has not been reviewed yet.

rating distribution
average user rating0.0 out of 5.0 based on 0 reviews
    Please log in to take part in the discussion (add own reviews or comments).