Large-Scale Machine Learning with Stochastic Gradient Descent

Author:

Bottou Léon

Publisher

Physica-Verlag HD

Reference24 articles.

1. BORDES. A., BOTTOU, L., and GALLINARI, P. (2009): SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent. Journal of Machine Learning Research, 10:1737-1754. With Erratum (to appear).

2. BOTTOU, L. and BOUSQUET, O. (2008): The Tradeoffs of Large Scale Learning, In Advances in Neural Information Processing Systems, vol.20, 161-168.

3. BOTTOU, L. and LECUN, Y. (2004): On-line Learning for Very Large Datasets. Applied Stochastic Models in Business and Industry, 21(2):137-151

4. BOUSQUET, O. (2002): Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms. Thèse de doctorat, Ecole Polytechnique, Palaiseau, France.

5. CORTES, C. and VAPNIK, V. N. (1995): Support Vector Networks, Machine Learning, 20:273-297.

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