OASIS: Online active semi-supervised learning

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2011, Pages 362-367

  Goldberg, Andrew B ^a   Zhu, Xiaojin ^b   Furger, Alex ^b   Xu, Jun Ming ^b  

^a Arcode Corporation   (United States)

^b University of Wisconsin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; BAYESIAN MODEL; BAYESIAN UPDATING; LABELED AND UNLABELED DATA; LEARNING MODELS; LEARNING SETTINGS; LIKELIHOOD FUNCTIONS; REAL-WORLD APPLICATION; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SEQUENTIAL MONTE CARLO;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; OPTICAL CHARACTER RECOGNITION;

SUPERVISED LEARNING;

EID: 80055035242     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Beygelzimer, A.; Dasgupta, S.; and Langford, J. 2009. Importance weighted active learning. In ICML.
2. Chapelle, O., and Zien, A. 2005. Semi-supervised classification by low density separation. In AISTAT 2005.
3. Chapelle, O.; Sindhwani, V.; and Keerthi, S. S. 2008. Optimization techniques for semi-supervised support vector machines. JMLR 9(Feb):203-233. (Pubitemid 351469022)
4. Crammer, K.; Kulesza, A.; and Dredze, M. 2009. Adaptive regularization of weight vectors. In NIPS 22.
5. Doucet, A., and Johansen, A. M. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. In Crisan, D., and Rozovsky, B., eds., Handbook of Nonlinear Filtering. Oxford University Press.
6. Doucet, A.; De Freitas, N.; and Gordon, N., eds. 2001. Sequential Monte Carlo methods in practice.
7. Furao, S.; Sakurai, K.; Kamiya, Y.; and Hasegawa, O. 2007. An online semi-supervised active learning algorithm with self-organizing incremental neural network. In IJCNN.
8. Gelman, A.; Jakulin, A.; Pittau, M. G.; and Su, Y.-S. 2008. A weakly informative default prior distribution for logistic and other regression models. Annals of Applied Statistics 2(4):1360-1383.
9. Gilks, W. R., and Berzuini, C. 2001. Following a moving target - Monte Carlo inference for dynamic Bayesian models. Journal Of The Royal Statistical Society Series B 63(1):127-146. (Pubitemid 33392467)
10. Goldberg, A. B.; Li, M.; and Zhu, X. 2008. Online manifold regularization: A new learning setting and empirical study. In ECML PKDD.
11. Grabner, H.; Leistner, C.; and Bischof, H. 2008. Semi-supervised on-line boosting for robust tracking. In ECCV.
12. Hastings, W. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97-109.
13. Lawrence, N. D., and Jordan, M. I. 2004. Semi-supervised learning via Gaussian processes. In NIPS 17.
14. Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; and Teller, E. 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics 21:1087-1092.
15. Nowak, R. 2009. Noisy generalized binary search. In NIPS 22.
16. Rahimi, A., and Recht, B. 2007. Random features for large-scale kernel machines. In NIPS 20.
17. Ridgeway, G., and Madigan, D. 2003. A sequential Monte Carlo method for Bayesian analysis of massive datasets. Journal of Data Mining and Knowledge Discovery 7(3):301-319.
18. Singh, A.; Nowak, R.; and Zhu, X. 2008. Unlabeled data: Now it helps, now it doesn't. In NIPS 21.
19. Tang, F.; Brennan, S.; Zhao, Q.; and Tao, H. 2007. Co-tracking using semi-supervised support vector machines. In ICCV.
20. Valko, M.; Kveton, B.; Huang, L.; and Ting, D. 2010. Online semi-supervised learning on quantized graphs. In UAI.