title: The general inefficiency of batch training for gradient descent learning.
abstract: 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.
authors: Fonix Corporation, 180 West Election Road Suite 200, Draper, UT, USA. randy@axon.cs.byu.edu
authors: D Randall
authors: DR
authors: Wilson
authors: Tony R
authors: TR
authors: Martinez
mesh_terms: D000465:Algorithms; D005793:Generalization, Psychological; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D009862:Online Systems; D011581:Psychological Tests; D021641:Recognition, Psychology; D012196:Review Literature as Topic; D013067:Speech Perception; D013269:Stochastic Processes; D013663:Teaching; D013997:Time Factors