Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Active Learning Theory

  • Sanjoy Dasgupta
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_7

Definition

The term active learning applies to a wide range of situations in which a learner is able to exert some control over its source of data. For instance, when fitting a regression function, the learner may itself supply a set of data points at which to measure response values, in the hope of reducing the variance of its estimate. Such problems have been studied for many decades under the rubric of experimental design (Chernoff, 1972; Fedorov, 1972). More recently, there has been substantial interest within the machine learning community in the specific task of actively learning binary classifiers. This task presents several fundamental statistical and algorithmic challenges, and an understanding of its mathematical underpinnings is only gradually emerging. This brief survey will describe some of the progress that has been made so far.

Learning from Labeled and Unlabeled Data

In the machine learning literature, the task of learning a classifier has traditionally been studied in...

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Recommended Reading

  1. Angluin, D. (2001). Queries revisited. In Proceedings of the 12th international conference on algorithmic learning theory (pp. 12–31).Google Scholar
  2. Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In International Conference on Machine Learning (pp. 65–72). New York: ACM Press.Google Scholar
  3. Balcan, M.-F., Broder, A., & Zhang, T. (2007). Margin based active learning. In Conference on Learning Theory. pp. 35–50.Google Scholar
  4. Baum, E. B., & Lang, K. (1992). Query learning can work poorly when a human oracle is used. In International Joint Conference on Neural Networks.Google Scholar
  5. Beygelzimer, A., Dasgupta, S., & Langford, J. (2009). Importance weighted active learning. In International Conference on Machine Learning (pp. 49–56). New York: ACM Press.Google Scholar
  6. Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2004). Worst-case analysis of selective sampling for linear-threshold algorithms. Advances in Neural Information Processing Systems.Google Scholar
  7. Chernoff, H. (1972). Sequential analysis and optimal design. In CBMS-NSF Regional Conference Series in Applied Mathema- tics 8. SIAM.Google Scholar
  8. Cohn, D., Atlas, L., & Ladner, R. (1994). Improving generalization with active learning. Machine Learning, 15(2),201–221.Google Scholar
  9. Dasgupta, S. (2005). Coarse sample complexity bounds for active learning. Advances in Neural Information Processing Systems.Google Scholar
  10. Dasgupta, S., Kalai, A., & Monteleoni, C. (2005). Analysis of perceptron-based active learning. In 18th Annual Conference on Learning Theory. pp. 249–263.Google Scholar
  11. Dasgupta, S., Hsu, D. J., & Monteleoni, C. (2007). A general agnostic active learning algorithm. Advances in Neural Information Processing Systems.Google Scholar
  12. Fedorov, V. V. (1972). Theory of optimal experiments. (W. J. Studden & E. M. Klimko, Trans.). New York: Academic Press.Google Scholar
  13. Freund, Y., Seung, S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning Journal, 28,133–168.zbMATHGoogle Scholar
  14. Friedman, E. (2009). Active learning for smooth problems. In Conference on Learning Theory. pp. 343–352.Google Scholar
  15. Gilad-Bachrach, R., Navot, A., & Tishby, N. (2005). Query by committeee made real. Advances in Neural Information Processing Systems.Google Scholar
  16. Hanneke, S. (2007a). Teaching dimension and the complexity of active learning. In Conference on Learning Theory. pp. 66–81.Google Scholar
  17. Hanneke, S. (2007b). A bound on the label complexity of agnostic active learning. In International Conference on Machine Learning. pp. 353–360.Google Scholar
  18. Haussler, D. (1992). Decision-theoretic generalizations of the PAC model for neural net and other learning applications. Information and Computation, 100(1),78–150.zbMATHMathSciNetGoogle Scholar
  19. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Conference on Computational Learning Theory, pp. 287–294.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Sanjoy Dasgupta

There are no affiliations available