Explicit update vs implicit update

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 3441-3447

  He, Wenwu ^a,b   Jiang, Hui ^c  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b FUJIAN UNIVERSITY OF TECHNOLOGY   (China)

^c HUIZHOU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

E-LEARNING; INTERNET;

CONVERGENCE BOUNDS; ON-LINE LEARNINGS; UPDATE TECHNIQUES;

NEURAL NETWORKS;

EID: 56349084685     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4634288     Document Type: Conference Paper

References (21)

1. J. Kivinen, A. J. Smola, and R. C. Williamson, "Online learning with kernels," Advances in Neural Information Processing Systems, vol. 14, pp. 785-792, 2002.
2. J. Kivinen, A. J. Smola, and R.C.Williamson, "Online learning with kernels," IEEE Transactions on Signal Processing, vol. 52, no. 8, Aug. 2004.
3. S. Smale and Y. Yao, "Online Learning Algorithms," Foundations of Computational Mathematics, vol. 6, no. 2, pp. 145-170, 2006.
4. Y. Ying and M. Pontil, "Online gradient descent learning algorithms," Foundations of Computational Mathematics, Online version, 25 Apr. 2007.
5. J. Kivinen and M. K.Warmuth, "Exponentiated gradient versus gradient descent for linear predictors," Information and Computation, vol. 132, no. 1, pp. 1-64, Jan. 1997.
6. Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, and Terry Caelli, "Implicit Online Learning With Kernels," Advances in Neural Information Processing Systems, vol. 19, 2006.
7. S. V. N. Vishwanathan, N. N. Schraudolph, and A. J. Smola, "Step size adaptation in reproducing kernel Hilbert space," Journal of Machine Learning Research, vol. 7, pp. 1107-1133, Jun. 2006.
8. K. I. Kim, M. O. Franz, and B. Schölkopf, "Iterative kernel principal component analysis for image modeling," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1351-1366, 2005.
9. N. N. Schraudolph, "Local gain adaptation in stochastic gradient descent," Proc. Intl. Conf. Artificial Neural Networks, pp. 569-574, 1999.
10. R. S. Sutton, "Gain adaptation beats least squares?" Proceedings of the 7th Yale Workshop on Adaptive and Learning Systems, pp. 161-166, 1992.
11. N. N. Schraudolph, "Fast curvature matrix-vector products for second-order gradient descent," Neural Computation, vol. .14, no. 7, pp. 1723-1738, 2002.
12. N. N. Schraudolph and X. Giannakopoulos, "Online independent component analysis with local learning rate adaptation," Neural Information Processing Systems, vol. 12, pp. 789-795, 2000.
13. M. Bray, E. Koller-Meier, P. Muller, N. N. Schraudolph, and L. Van Gool, "Stochastic optimization for high-dimensional tracking in dense range maps," IEE Proceedings Vision, Image & Signal Processing, vol. 152, no. 4, pp. 501-512, 2005.
14. Bray, E. Koller-Meier, N. N. Schraudolph, and L. Van. Gool, "Fast stochastic optimization for articulated structure tracking," Image and Vision Computing, vol. 25, no.3, pp. 352-364, 2007.
15. N. N. Schraudolph, J. Yu, and D. Aberdeen, "Fast online policy gradient learning with SMD gain vector adaptation," Advances in Neural Information Processing Systems, vol. 18, 2006.
16. S. V. N. Vishwanathan, N. N. Schraudolph, Mark Schmidt, and Kevin Murphy, "Accelerated training conditional random fields with stochastic gradient methods," Proc. Intl. Conf. Machine Learning, pp. 969-976, 2006.
17. S.K. Oh, M.S. Kim, T.D. Eom, and. J.J. Lee, "Heterogeneous local model networks for time series prediction," Applid Mathematics and Computation, vol. 168, pp. 164-177, 2005.
18. W. He and Z. Wang, "Model optimizing and feature selecting for support vector regression in time series forecasting," Nurocomputing, doi:10.1016/j.neucom.2007.11.0.10.
19. W. He and Z. Wang, "Fast Forecasting with Simplified Kernel Regression Machines," Proceddings of the 2007 International Conference on Computational Intelligence and Security, pp. 60-64, 2007.
20. H. Jäske, "One-step-ahead prediction of sunspots with genetic programming," Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications, pp. 79-88, 1996.
21. K. Chellapilla and S.S. Rao, "Optimization of bilinear time series models using fast evolutionary programming," IEEE Transaction on Signed Processing Letters, vol. 5, no. 2, pp. 39-42, 1998.