Active sequential learning with tactile feedback

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 677-684

  Saal, Hannes P ^a   Ting, Jo Anne ^b   Vijayakumar, Sethu ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; DATA SAMPLE; GAUSSIAN APPROXIMATIONS; HAND-ARM SYSTEM; HIGH-DIMENSIONAL; INFORMATION THEORETIC CRITERION; MONTE CARLO SAMPLING; OPTIMAL ACTIONS; SEQUENTIAL LEARNING; STATE PARAMETERS; TACTILE FEEDBACK; TIME STEP;

ARTIFICIAL INTELLIGENCE; INFORMATION THEORY; MONTE CARLO METHODS;

SLIP FORMING;

EID: 78651519903     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (20)

1. Amari, S., Cichocki, A., & Yang, H. H. (1994). A new learning algorithm for blind source separation. In Advances in Neural Information Processing Systems (pp. 757-763). MIT Press.
2. Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129-145.
3. Dagan, I., & Engelson, S. (1995). Committeebased sampling for training probabilistic classifiers. In International Conference on Machine Learning (pp. 150-157). Morgan Kaufmann.
4. Deisenroth, M. P., Huber, M. F., & Hanebeck, U. D. (2009). Analytic moment-based gaussian process filtering. In International Conference on Machine Learning.
5. Denzler, J., & Brown, C. M. (2002). Information theoretic sensor data selection for active object recognition and state estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2), 145-157.
6. Doucet, A., Freitas, N. de, & Gordon, N. (2001). Sequential Monte Carlo methods in practice. Springer.
7. Fookes, C., & Maeder, A. J. (2004). Quadraturebased image registration method using mutual information. In IEEE International Symposium on Biomedical Imaging (p. 728-731). IEEE.
8. Girard, A., Rasmussen, C. E., Candela, J. Q., & Murray-Smith, R. (2003). Gaussian process priors with uncertain inputs - application to multiple-step ahead time series forecasting. In Advances in Neural Information Processing Systems (p. 545-552). MIT Press.
9. Guo, Y., & Greiner, R. (2007). Optimistic active learning using mutual information. In International Joint Conference on Artificial Intelligence (pp. 823-829). AAAI Press.
10. Lewi, J., Butera, R. J., & Paninski, L. (2009). Sequential optimal design of neurophysiology experiments. Neural Computation, 21(3), 619-687.
11. Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. In ACM SIGIR Conference on Research and Development (pp. 3-12). Springer-Verlag.
12. Lindley, D. (1956). On a measure of information provided by an experiment. Annals of Mathematical Statistics, 29, 986-1005.
13. Mackay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4, 589-603.
14. McCallum, A., & Nigam, K. (1998). Employing EM in pool-based active learning for text classification. In International Conference on Machine Learning (pp. 359-367). Morgan Kaufmann.
15. Paninski, L. (2005). Asymptotic theory of information-theoretic experimental design. Neural Computation, 17(7), 1480-1507.
16. Roy, N., & McCallum, N. (2001). Toward optimal active learning through sampling estimation of error reduction. In International Conference on Machine Learning (pp. 441-448). Morgan Kaufmann.
17. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In ACM Workshop on Computational Learning Theory (pp. 287- 294).
18. Torkkola, K. (2003). Feature extraction by nonparametric mutual information maximization. Journal of Machine Learning Research, 3.
19. Williams, C. K. I., & Rasmussen, C. E. (1995). Gaussian processes for regression. In Advances in Neural Information Processing Systems. MIT Press.
20. Zhu, X., Lafferty, J., & Ghahramani, Z. (2003). Combining active learning and semisupervised learning using gaussian fields and harmonic functions. In ICML Workshop on the Continuum from Labeled to Unlabeled Data (pp. 58-65).