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Cited by 791

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Incremental Induction of Decision Trees

Open access
Utgoff, Paul E.·1989·Machine Learning
DOI 10.1023/a:1022699900025
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Citations

Cites

Research works printed but not linked: 19

  1. Barto, A. G. (1985). Learning by statistical cooperation of self-interested neuron-like computing elements. Human Neurobiology, 4, 229–256.
  2. Buchanan, B. G., and Mitchell, T. M. (1978). Model-directed learning of production rules. In D. A. Waterman and F. Hayes-Roth (Eds.), Pattern-directed inference systems. New York: Academic Press.
  3. Cockett, J. R. B. (1987). Discrete decision theory: Manipulations. Theoretical Computer Science, 54, 215–236. · DOI
  4. Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139–172. · DOI · Paper page
  5. Gallant, S. I. (1988). Connectionist expert systems. Communications of the ACM, 31, 152–169. · DOI · Paper page
  6. Lewis, P. M. (1962). The characteristic selection problem in recognition systems. IRE Transactions on Information Theory, IT-8, 171–178. · DOI
  7. Michalski, R. S., and Chilausky, R. L. (1980). 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, 4, 125–160.
  8. Michalski, R. S. (1983). A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. San Mateo, CA: Morgan Kaufmann. · DOI · Paper page
  9. Mitchell, T. M. (1978). Version spaces: An approach to concept learning. Doctoral dissertation, Department of Electrical Engineering, Stanford University, Palo Alto, CA.
  10. Moret, B. M. E. (1982). Decision trees and diagrams. Computing Surveys, 14, 593–623. · DOI
  11. Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. San Mateo, CA: Morgan Kaufmann.
  12. Quinlan, J. R. (1988). An empirical comparison of genetic and decision-tree classifiers. Proceedings of the Fifth International Conference on Machine Learning (pp. 135–141). Ann Arbor, MI: Morgan Kaufmann.
  13. Schlimmer, J. C., and Fisher, D. (1986). A case study of incremental concept induction. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 496–501). Philadelphia, PA: Morgan Kaufmann.
  14. Schlimmer, J. C., and Granger, R. H., Jr. (1986). Incremental learning from noisy data. Machine Learning, 1, 317–354.
  15. Utgoff, P. E. (1988). ID5: An incremental ID3. Proceedings of the Fifth International Conference on Machine Learning (pp. 107–120). Ann Arbor, MI: Morgan Kaufmann.
  16. Utgoff, P. E. (1989). Improved training via incremental learning. Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann.
  17. Vere, S. A. (1980). Multilevel counterfactuals for generalizations of relational concepts and productions. Artificial Intelligence, 14, 138–164.
  18. Watanabe, S. (1985). Pattern recognition: Human and mechanical. New York: Wiley and Sons.
  19. Wilson, S. W. (1987). Classifier systems and the animal problem. Machine Learning, 2, 199–228. · DOI · Paper page

Cited by

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Metadata

Authors
Utgoff, P. E.
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
1989-11
Volume
4
Issue
2
Pages
161-186
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1023/a:1022699900025
OpenAlex
W2112841646
Cited by
791
References
3
FWCI
40.49
Type
journal article
Language
en
Open access
bronze
Licence

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