Skip to main content

Active Learning

  • Reference work entry
Encyclopedia of Machine Learning

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Recommended Reading

  • Angluin, D. (1987). Learning regular sets from queries and counterexamples. Information and Computation, 75(2), 87–106.

    MATH  MathSciNet  Google Scholar 

  • Angluin, D. (1988). Queries and concept learning. Machine Learning, 2, 319–342.

    Google Scholar 

  • Box, G. E. P., & Draper, N. (1987). Empirical model-building and response surfaces. New York: Wiley.

    MATH  Google Scholar 

  • Cleveland, W., Devlin, S., & Gross, E. (1988). Regression by local fitting. Journal of Econometrics, 37, 87–114.

    MathSciNet  Google Scholar 

  • Cohn, D., Atlas, L., & Ladner, R. (1990). Training connectionist networks with queries and selective sampling. In D. Touretzky (Ed.)., Advances in neural information processing systems. Morgan Kaufmann.

    Google Scholar 

  • Cohn, D., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129–145. http://citeseer.ist.psu.edu/321503.html

  • Dasgupta, S. (1999). Learning mixtures of Gaussians. Foundations of Computer Science, 634–644.

    Google Scholar 

  • Fedorov, V. (1972). Theory of optimal experiments. New York: Academic Press.

    Google Scholar 

  • Kearns, M., Li, M., Pitt, L., & Valiant, L. (1987). On the learnability of Boolean formulae, Proceedings of the 19th annual ACM conference on theory of computing (pp. 285–295). New York: ACM Press.

    Google Scholar 

  • Lewis, D. D., & Gail, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the 17th annual international ACM SIGIR conference (pp. 3–12). Dublin.

    Google Scholar 

  • McCallum, A., & Nigam, K. (1998). Employing EM and pool-based active learning for text classification. In Machine learning: Proceedings of the fifteenth international conference (ICML’98) (pp. 359–367).

    Google Scholar 

  • North, D. W. (1968). A tutorial introduction to decision theory. IEEE Transactions Systems Science and Cybernetics, 4(3).

    Google Scholar 

  • Pitt, L., & Valiant, L. G. (1988). Computational limitations on learning from examples. Journal of the ACM (JACM), 35(4), 965–984.

    MATH  MathSciNet  Google Scholar 

  • Robbins, H. (1952). Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 55, 527–535.

    MathSciNet  Google Scholar 

  • Ruff, R., & Dietterich, T. (1989). What good are experiments? Proceedings of the sixth international workshop on machine learning. Ithaca, NY.

    Google Scholar 

  • Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the fifth workshop on computational learning theory (pp. 287–294). San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Steck, H., & Jaakkola, T. (2002). Unsupervised active learning in large domains. In Proceeding of the conference on uncertainty in AI. http://citeseer.ist.psu.edu/steck02unsupervised.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this entry

Cite this entry

Cohn, D. (2011). Active Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_6

Download citation

Publish with us

Policies and ethics