Distributed training of large-scale logistic models

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 2, 2013, Pages 948-956

  Gopal, Siddharth ^a   Yang, Yiming ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

COMPUTATIONAL COSTS; DATA CLASSIFICATION; LARGE-SCALE DATASETS; LARGE-SCALE LOGISTICS; MAGNITUDE REDUCTION; MULTINOMIAL LOGISTIC REGRESSION; MULTINOMIAL LOGISTICS; PARALLEL OPTIMIZATION;

OPTIMIZATION;

EID: 84897475251     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Blei, D.M. and Lafferty, J.D. Dynamic topic models. In ICML, pp. 113-120. ACM, 2006.
2. Bottou, L. Online learning and stochastic approximations. On-line learning in neural networks, 1998.
3. Bottou, L. Large-scale machine learning with stochastic gradient descent. In Compstat, 2010.
4. Bouchard, G. Efficient bounds for the softmax function, applications to inference in hybrid models. 2007.
5. Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 2011.
6. Darroch, J.N. and Ratcliff, D. Generalized iterative scaling for log-linear models. The annals of mathematical statistics, 43(5):1470-1480, 1972.
7. Daumé III, H. Notes on eg and lm-bfgs optimization of logistic regression. 2004.
8. Holland, P.W. and Welsch, R.E. Robust regression using iteratively reweighted least-squares. CSTM, 1977.
9. Hsiung, K.L., Kim, S.J., and Boyd, S. Tractable approximate robust geometric programming. Optimization and Engineering, 9(2):95-118, 2008. (Pubitemid 351460135)
10. Kirkwood, B.R. et al. Essentials of medical statistics. Blackwell Scientific Publications, 1988.
11. Lin, C.J., Weng, R.C., and Keerthi, S.S. Trust region newton method for logistic regression. The Journal of Machine Learning Research, 9:627-650, 2008.
12. Liu, D.C. and Nocedal, J. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45(1):503-528, 1989. (Pubitemid 20660315)
13. Marlin, B., Khan, M.E., and Murphy, K. Piecewise bounds for estimating bernoulli-logistic latent gaussian models. In ICML, 2011.
14. Minka, T.P. A comparison of numerical optimizers for logistic regression. Unpublished draft, 2003.
15. Rust, R.T. and Zahorik, A.J. Customer satisfaction, customer retention, and market share. Journal of retailing, 69(2):193-215, 1993.
16. Schraudolph, N., Yu, J., and Günter, S. A stochastic quasi-newton method for online convex optimization. 2007.
17. Sha, F. and Pereira, F. Shallow parsing with conditional random fields. In NAACL, pp. 134-141. Association for Computational Linguistics, 2003.
18. Shanno, D.F. On broyden-fletcher-goldfarb-shanno method. Journal of Optimization Theory and Applications, 46(1):87-94, 1985.
19. Yu, H.F., Huang, F.L., and Lin, C.J. Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning, 85(1):41-75, 2011.
20. Zhang, T. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In ICML, pp. 116. ACM, 2004.