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- Title
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent.
- Authors
Bordes, Antoine; Bottou, Léon; Gallinari, Patrick
- Abstract
The SGD-QN algorithm is a stochastic gradient descent algorithm that makes careful use of second-order information and splits the parameter update into independently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first-order stochastic gradient descent but requires less iterations to achieve the same accuracy. This algorithm won the "Wild Track" of the first PASCAL Large Scale Learning Challenge (Sonnenburg et al., 2008).
- Subjects
ALGORITHMS; STOCHASTIC processes; THEORY of descent (Mathematics); SUPPORT vector machines; STOCHASTIC orders; ITERATIVE methods (Mathematics)
- Publication
Journal of Machine Learning Research, 2009, Vol 10, Issue 7, p1737
- ISSN
1532-4435
- Publication type
Article