Inference from aging information

IEEE Transactions on Neural Networks

Volumn 21, Issue 6, 2010, Pages 1015-1020

  De Oliveira, Evaldo Araújo ^a   Caticha, Nestor ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Online Bayesian algorithms; Pattern classification; Timevarying environment

Indexed keywords

AD HOC METHODS; ADAPTIVE WINDOW SIZE; BAYESIAN; BAYESIAN ALGORITHMS; DATA COLLECTION; DATA DISTRIBUTION; INFORMATION GEOMETRY; LEARNING MACHINES; LEARNING TASKS; LOCAL TRAPS; MEMORY WINDOW; PATTERN CLASSIFICATION; POSTERIOR DISTRIBUTIONS; THEORETICAL APPROACH; TIME-SCALES; TIME-VARYING ENVIRONMENTS;

ADAPTIVE ALGORITHMS; ALGEBRA; BAYESIAN NETWORKS;

LEARNING ALGORITHMS;

AGING; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; HUMAN; INFORMATION SCIENCE; LEARNING; SYSTEM ANALYSIS;

AGING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; INFORMATION THEORY; LEARNING; PROBABILITY THEORY;

EID: 77953101472     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2046422     Document Type: Article

References (16)

1. A. Giffin and A. Caticha, "Updating probabilities with data and moments," in Proc. Bayesian Inference Maximum Entropy Methods Sci. Eng. Conf., K. H. Knuth, A. Caticha, J. L. Center, A. Giffin, and C. C. Rodriguez, Eds., Nov. 2007, vol.954, pp. 74-84.
2. J. Schlimmer and R. G. Jr, "Incremental learning from noisy data," Mach. Learn., vol.1, no.3, pp. 317-354, 1986.
3. I. Koychev and R. Lothian, "Tracking drifting concepts by time window optimisation," in Proc. Res. Develop. Intell. Syst. XXII/25th SGAI Int. Conf. Innovative Tech. Appl. Artif. Intell., M. Bramer, F. Coenen, and T. Allen, Eds., 2006, pp. 46-59.
4. M. Biehl and H. Schwarze, "Learning drifting concepts with neural networks," J. Phys. A, Math. Gen., vol.26, pp. 2651-2665, June 1993.
5. O. Kinouchi and N. Caticha, "Lower bounds on generalization errors for drifting rules," J. Phys. A, Math. Gen., vol.26, pp. 6161-6171, Nov. 1993.
6. R. Vicente, O. Kinouchi, and N. Caticha, "Statistical mechanics of online learning of drifting concepts: A variational approach," Mach. Learn., vol.32, pp. 179-201, Aug. 1998. (Pubitemid 40626080)
7. N. Cesa-Bianchi and G. Lugosi, Prediction, Learning, and Games. Cambridge, U.K.: Cambridge Univ. Press, Mar. 2006.
8. M. Opper, "A Bayesian approach to on-line learning," in On-Line Learning in Neural Networks, D. Saad, Ed. Cambridge, U.K.: Cambridge Univ. Press, Apr. 1998, pp. 363-378.
9. S. Solla and O. Winther, "Optimal perceptron learning: An online Bayesian approach," in On-line Learning in Neural Networks, D. Saad, Ed. Cambridge, U.K.: Cambridge Univ. Press, Apr. 1998, pp. 379-398.
10. N. Caticha and E. A. de Oliveira, "Gradient descent learning in and out of equilibrium," Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top., vol.63, no.6, p. 061905, 2001.
11. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," Mach. Learn., vol.23, pp. 69-101, Apr. 1996.
12. J. Gama, P. Medas, G. Castilho, and P. Rodrigues, "Learning with drift detection," in Proc. 17th Brazilian Symp. Artif. Intell., Berlin, Germany, Nov. 2004, vol.3171, pp. 285-295.
13. M. Murata, M. Kawanabe, A. Ziehe, K. Müller, and S. Amari, "On-line learning in changing environments with applications in supervised and unsupervised learning," Neural Netw., vol.15, pp. 743-760, Jun. 2002.
14. E. A. de Oliveira and R. C. Alamino, "Performance of the Bayesian online algorithm for the perceptron," IEEE Trans. Neural Netw., vol.18, no.3, pp. 902-905, May 2007.
15. E. A. de Oliveira, "The Rosenblatt Bayesian algorithm learning in a nonstationary environment," IEEE Trans. Neural Netw., vol.18, no.2, pp. 584-588, Mar. 2007.
16. O. Kinouchi and N. Caticha, "Optimal generalization in perceptrons," J. Phys. A, Math. Gen., vol.25, pp. 6243-6250, 1992.