Skip to main content
SpringerLink
Log in
Book cover

Machine Learning pp 41–81Cite as

A Comparative Review of Selected Methods for Learning from Examples

  • Chapter

Part of the book series: Symbolic Computation ((1064))

Abstract

Research in the area of learning structural descriptions from examples is reviewed, giving primary attention to methods of learning characteristic descriptions of single concepts. In particular, we examine methods for finding the maximally-specific conjunctive generalizations (MSC-generalizations) that cover all of the training examples of a given concept. Various important aspects of structural learning in general are examined, and several criteria for evaluating structural learning methods are presented. Briefly, these criteria include (i) adequacy of the representation language, (ii) generalization rules employed, (iii) computational efficiency, and (iv) flexibility and extensibility. Selected learning methods developed by Buchanan, et al., Hayes-Roth, Vere, Winston, and the authors are analyzed according to these criteria. Finally, some goals are suggested for future research.

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   109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
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

  • Biermann, A. and Feldman, J., A survey of results in grammatical inference, Academic Press, New York, 1972.

    Google Scholar 

  • Buchanan, B. G., Feigenbaum, E. A. and Lederberg, J., “A heuristic programming study of theory formation in sciences,” Proceedings of the Second International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, London, pp. 40–48, 1971.

    Google Scholar 

  • Buchanan, B. G., Smith, D. H., White, W. C., Gritter, R. J., Feigenbaum, E. A., Lederberg, J., and Djerassi, C., “Applications of artificial intelligence for chemical inference xxtt. Automatic rule formation in mass spectrometry by means of the Meta-DENDRAL program,” Journal of the American Chemical Society, Vol. 98, pp. 61–68, 1976.

    Article  Google Scholar 

  • Cohen, B. L. and Sammut, C. A., “Pattern recognition and learning with a structural description language,” Proceedings of the Fourth International Joint Conference on Artificial Intelligence, IJCPR, Kyoto, Japan, pp. 394, 1978.

    Google Scholar 

  • Dietterich, T. and Michalski, R., “Inductive Learning of Structural Descriptions,” Artificial Intelligence, Vol. 16, 1981.

    Google Scholar 

  • Hayes-Roth, F, F., “Collected papers on the learning and recognition of structured patterns”, Technical Report technical report, Carnegie-Mellon Department of Computer Science, Pittsburgh, PA., May 1976.

    Google Scholar 

  • Hayes-Roth, F., “Patterns of induction and associated knowledge acquisition algorithms”, Technical Report, Carnegie-Mellon University, May 1976.

    Google Scholar 

  • Hayes-Roth, F. and McDermott, J., “Knowledge acquisition from structural descriptions,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI, Cambridge, Mass., pp. 356–362, August 1977.

    Google Scholar 

  • Hayes-Roth, F. and McDermott, J., “An interference matching technique for inducing abstractions,” Communications of the ACM, Vol. 21, No. 5, pp. 401–410, 1978.

    Article  MATH  Google Scholar 

  • Hunt, E. B., Marin, J. and Stone, P. T., Experiments in Induction, Academic Press, New York, 1966.

    Google Scholar 

  • Iba, G. A., “Learning disjunctive concepts from examples,” Master’s thesis, M.I.T., Cambridge, Mass., 1979, (also Al memo 548).

    Google Scholar 

  • Knapman, J., “A critical review of Winston’s learning structural descriptions from examples,” AISB Quarterly, Vol. 31, pp. 319–320, September 1978.

    Google Scholar 

  • Larson, J., Inductive inference in the variable-valued predicate logic system VL21: methodology and computer implementation, Ph.D. dissertation, University of Illinois, Urbana, Illinois, May 1977.

    Google Scholar 

  • Larson, J. and Michalski, R. S., “Inductive inference of VL decision rules,” Proceedings of the Workshop on Pattern Directed Inference Systems, SIGART Newsletter 63, pp. 38–44, June 1977.

    Article  Google Scholar 

  • Lenat, D. B., AM: an artificial intelligence approach to discovery in mathematics as heuristic search, Ph.D. dissertation, Stanford University, Stanford, California, 1976.

    Google Scholar 

  • Michalski, R. S., “Discovering classification rules using variable-valued logic system VL1,” Proceedings of the Third International Joint Conference on Artificial Intelligence, IJCAI, pp. 162–172, 1973.

    Google Scholar 

  • Michalski, R. S., “Discovering classification rules using variable-valued logic system VL1,” Proceedings of the Third International Joint Conference on Artificial Intelligence, IJCAI, pp. 162–172, 1973a.

    Google Scholar 

  • Michalski, R. S. S., “Variable-Valued Logic and its Applications to Pattern Recognition and Machine Learning,” Multiple-Valued Logic and Computer Science, Rine, D. (Ed.), North-Holland, pp. 506–534, 1975a.

    Google Scholar 

  • Michalski, R. S., “Pattern recognition as rule-guided inductive inference,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 4, pp. 349–361, 1980a.

    Article  MathSciNet  Google Scholar 

  • Michalski, R. S. and Chilausky, R. L., “Learning by being told and learning from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis,” Policy Analysis and Information Systems, Vol. 4, No. 2, pp. 125–160, June 1980, ( Special issue on knowledge acquisition and induction).

    Google Scholar 

  • Mitchell, T. M., “Version Spaces: A candidate elimination approach to rule learning,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI, Cambridge, Mass., pp. 305–310, 1977.

    Google Scholar 

  • Mitchell, T. M., Version Spaces: An approach to concept learning,Ph.D. dissertation, Stanford University, December 1978, (also Stanford CS report STAN-CS-78–711, HPP-79–2).

    Google Scholar 

  • Plotkin, G. D. D., “A note on inductive generalization,” Machine Intelligence, Meltzer, B. and’ Michie, D. (Eds.), Edinburgh University Press, Edinburgh, pp. 153–163, 1970.

    Google Scholar 

  • Plotkin, G. D. D., “A further note on inductive generalization,” Machine Intelligence, Meltzer, B. and Michie, D. (Eds.), Elsevier, Edinburgh, pp. 101–124, 1971.

    Google Scholar 

  • Quinlan, J. R., “Discovering rules from large collections of examples: a case study,” Expert Systems in the Micro Electronic Age, Michie, D. (Ed.), Edinburgh University Press, Edinburgh, 1979.

    Google Scholar 

  • Quinlan, J. R., “Induction over large data bases”, Technical Report Report HPP-79–14, Heuristic Programming Project, Stanford University, 1979.

    Google Scholar 

  • Rubin, S. M., and Reddy, R., “The locus model of search and its use in image interpretation,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI, pp. 590–595, 1977.

    Google Scholar 

  • Sacerdoti, E., “Planning in a hierarchy of abstraction spaces,” Proceedings of the Third International Joint Conference on Artificial Intelligence, UCAI, pp. 412–422, 1973.

    Google Scholar 

  • Schwenzer, G. M., and Mitchell, T. M., Computer-assisted structure elucidation using automatically acquired carbon-13 NMR rules, American Chemical Society, 1977.

    Google Scholar 

  • Soloway, E. M., Learning interpretation + generalization: a case study in knowledge-directed learning,Ph.D. dissertation, University of Massachusetts at Amherst, 1978, (Computer and Information Science Report COINS TR-78–13).

    Google Scholar 

  • Stepp, R., “Learning without negative examples via variable-valued logic characterizations: the Uniclass inductive program AQ7UNI”, Technical Report 982, Department of Computer Science, University of Illinois at Urbana-Champaign, July 1979.

    Google Scholar 

  • Vere, S. A., “Induction of concepts in the predicate calculus,” Proceedings of the Fourth International Joint Conference on Artificial Intelligence, IJCAI, Tbilisi, USSR, pp. 281–287, 1975.

    Google Scholar 

  • Vere, S. A., “Induction of relational productions in the presence of background information,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI, Cambridge, Mass., pp. 349–355, 1977.

    Google Scholar 

  • Vere, S. A., “Inductive learning of relational productions,” Pattern-Directed Inference Systems, Waterman, D. A. and Hayes-Roth, F. (Eds.), Academic Press, New York, 1978.

    Google Scholar 

  • Vere, S. A., “Multilevel counterfactuals for generalizations of relational concepts and productions,” Artificial Intelligence, Vol. 14, No. 2, pp. 138–164, September 1980.

    Article  Google Scholar 

  • Winston, P. H. H., “Learning structural descriptions from examples”, Technical Report AI-TR-231, MIT, Cambridge, Mass., September 1970.

    Google Scholar 

  • Winston, P. H., “Learning structural descriptions from examples,” The Psychology of Computer Vision, Winston, P. H. (Ed.), McGraw Hill, New York, ch. 5, 1975, ( Original version published as a Ph.D. dissertaition, at MIT AI Lab, September, 1970 ).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Stanford University, USA

    Thomas G. Dietterich

  2. University of Illinois at Urbana-Champaign, USA

    Ryszard S. Michalski

Authors
  1. Thomas G. Dietterich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryszard S. Michalski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Illinois at Urbana-Champaign, IL, USA

    Ryszard S. Michalski (Professor of Computer Science and Medical Information Science, and the Director of the Artificial Intelligence Laboratory) (Professor of Computer Science and Medical Information Science, and the Director of the Artificial Intelligence Laboratory)

  2. Carnegie-Mellon University Pittsburgh, PA, USA

    Jaime G. Carbonell (Associate Professor in Computer Science) (Associate Professor in Computer Science)

  3. Rutgers University New Brunswick, NJ, USA

    Tom M. Mitchell (Associate Professor of Computer Science) (Associate Professor of Computer Science)

Rights and permissions

Reprints and permissions

Copyright information

© 1983 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dietterich, T.G., Michalski, R.S. (1983). A Comparative Review of Selected Methods for Learning from Examples. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds) Machine Learning. Symbolic Computation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-12405-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-12405-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-12407-9

  • Online ISBN: 978-3-662-12405-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation