Skip to main content
SpringerLink
Log in
Book cover

Advanced Course on Artificial Intelligence

ACAI 1999: Machine Learning and Its Applications pp 39–70Cite as

Unsupervised Learning of Probabilistic Concept Hierarchies

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2049))

Abstract

Since the field’s inception, most research in machine learning has focused on the problem of supervised induction from labeled training cases. If anything, this trend has been strengthened by the creation of data repositories that, typically, include class information. But this emphasis is misguided if we want to understand the nature of learning in intelligent agents like humans. Clearly, children acquire many concepts about the world before they learn names for them, and scientists regularly discover patterns without any clear supervision from an outside source. Even the availability of class labels in public data sets can be misleading; many such domains are medical in nature, and medical researchers first had to discover a disease before they could diagnose it for particular patients.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, J. R., & Matessa, M. (1991). An iterative Bayesian algorithm for categorization. In D. H. Fisher, M. J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  2. Binder, J., Koller, D., Russell, S., & Kanazawa, K. (1997). Adaptive probabilistic networks with hidden variables. Machine Learning, 29, 213–244.

    Article  MATH  Google Scholar 

  3. Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., & Freeman, D. (1988). Autoclass: A Bayesian classification system. Proceedings of the Fifth International Conference on Machine Learning (pp. 54–64). Ann Arbor, MI: Morgan Kaufmann.

    Google Scholar 

  4. Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309–347.

    MATH  Google Scholar 

  5. Day, D. S. (1992). Acquiring search heuristics automatically for constraint-based scheduling and planning. Proceedings of the First International Conference on AI Planning Systems (pp. 45–51). College Park, MD: Morgan Kaufmann.

    Google Scholar 

  6. Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139–172.

    Google Scholar 

  7. Fisher, D. H., Pazzani, M. J., & Langley, P. (Eds.) (1991). Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  8. Gennari, J. H. (1990). An experimental study of concept formation. Doctoral dissertation, Department of Information & Computer Science, University of California, Irvine.

    Google Scholar 

  9. Gennari, J. H., Langley, P., & Fisher, D. H. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11–61.

    Article  Google Scholar 

  10. Gluck, M., & Corter, J. (1985). Information, uncertainty and the utility of categories. Proceedings of the Seventh Annual Conference of the Cognitive Science Society (pp. 283–287). Irvine, CA: Lawrence Erlbaum.

    Google Scholar 

  11. Hadzikadic, M., & Yun, D. (1989). Concept formation by incremental conceptual clustering. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 831–836). Detroit, MI: Morgan Kaufmann.

    Google Scholar 

  12. Handa, K. (1990). Cfix: Concept formation by interaction of related objects. Proceedings of the Pacific Rim International Conference on Artificial Intelligence. Nagoya, Japan.

    Google Scholar 

  13. Hanson, R., Stutz, J., & Cheeseman, P. (1991). Bayesian classification with correlation and inheritance. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 692–698). Sydney: Morgan Kaufmann.

    Google Scholar 

  14. Heckerman, D. (1995). A tutorial on learning Bayesian networks (Technical Report MSR-TR-95-06). Redmond, WA: Microsoft Research.

    Google Scholar 

  15. Iba, W. (1991a). Learning to classify observed motor behavior. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 732–738). Sydney: Morgan Kaufmann.

    Google Scholar 

  16. Iba, W. (1991b). Acquisition and improvement of human motor skills: Learning through observation and practice. Doctoral dissertation, Department of Information & Computer Science, University of California, Irvine.

    Google Scholar 

  17. Kilander, F., & Jansson, C. G. (1993). Cobbit: A control procedure for Cobweb in the presence of concept drift. Proceedings of the 1993 European Conference on Machine Learning (pp. 244–261). Vienna: Springer-Verlag.

    Google Scholar 

  18. Langley, P., & Allen, J. A. (1991). Learning, memory, and search in planning. Proceedings of the Thirteenth Conference of the Cognitive Science Society (pp. 364–369). Chicago: Lawrence Erlbaum.

    Google Scholar 

  19. Langley, P., & Allen, J. A. (1993). A unified framework for planning and learning. In Minton (Ed.), Machine learning methods for planning and scheduling. San Mateo: Morgan Kaufmann.

    Google Scholar 

  20. Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. Proceedings of the Tenth National Conference on Artificial Intelligence (pp. 223–228). San Jose: AAAI Press.

    Google Scholar 

  21. Martin, J. D. (1992). Direct and indirect transfer: Explorations in concept formation. Doctoral dissertation, Department of Computer Science, Georgia Institute of Technology.

    Google Scholar 

  22. Martin, J. D. (1994). Goal-directed clustering. Proceedings of the AAAI Spring Symposium on Goal-Directed Learning. Stanford, CA.

    Google Scholar 

  23. Martin, J. D., & Billman, D. O. (1994). Acquiring and combining overlapping concepts. Machine Learning, 16, 121–155.

    Google Scholar 

  24. McKusick, K. B., & Langley, P. (1991). Constraints on tree structure in concept formation. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 810–816). Sydney: Morgan Kaufmann.

    Google Scholar 

  25. McKusick, K. B., & Thompson, K. (1990). Cobweb/3: A portable implementation (Tech. Rep. No. FIA-90-6-18-2). Moffett Field, CA: NASA Ames Research Center, AI Research Branch.

    Google Scholar 

  26. Provan, G. M., & Singh, M. (1995). Learning Bayesian networks using feature selection. Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics (pp. 450–456). Fort Lauderdale, FL.

    Google Scholar 

  27. Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81–106

    Google Scholar 

  28. Reich, Y., & Fenves, S. J. (1991). The formation and use of abstract concepts in design. In D. H. Fisher, M. J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  29. Thompson, K., & Langley, P. (1991). Concept formation in structured domains. In D. H. Fisher, M. J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  30. Yoo, J., & Fisher, D. H. (1991). Concept formation over problem-solving experience. In D. H. Fisher, M. J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto CA, 94306, USA

    Wayne Iba & Pat Langley

Authors
  1. Wayne Iba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pat Langley
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Centre for Scientific Research “Demokritos”, Institute of Informatics and Telecommunications, P.O. Box 60228, Ag. Paraskevi, 15310, Athens, Greece

    Georgios Paliouras , Vangelis Karkaletsis  & Constantine D. Spyropoulos ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Iba, W., Langley, P. (2001). Unsupervised Learning of Probabilistic Concept Hierarchies. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-44673-7_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42490-1

  • Online ISBN: 978-3-540-44673-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation