Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2,163

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Instance-based learning algorithms

Aha, David W., Kibler, Dennis, Albert, Marc K.·1991·Machine Learning
DOI 10.1007/bf00153759
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/bf00153759

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 45

  1. AhaD.W. (1989a). Incremental, instance-based learning of independent and graded concept descriptions. Proceedings of the Sixth International Workshop on Machine Learning (pp. 387?391). Ithaca, NY: Morgan Kaufmann. · DOI
  2. AhaD.W. (1989b). Incremental learning of independent, overlapping, and graded concepts with an instance-based process framework (Technical Report 89?10). Irvine, CA: University of California, Irvine, Department of Information and Computer Science.
  3. AhaD.W. (1989c). Tolerating noise, irrelevant attributes, and novel attributes in instance-based learning algorithms. Proceedings of the IJCAI-1989 Workshop on Symbolic Problem Solving in Noisy, Novel, and Uncertain Task Environments. Detroit, MI: Computing Research Laboratory, New Mexico State University.
  4. AhaD.W., & KiblerD. (1989). Noise-tolerant instance-based learning algorithms. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 794?799). Detroit, MI: Morgan Kaufmann.
  5. BareissE.R., PorterB., & WierC.C. (1987). PROTOS: An exemplar-based learning apprentice. Proceedings of the Fourth International Workshop on Machine Learning (pp. 12?23). Irvine, CA: Morgan Kaufmann. · DOI
  6. BlumerA., EhrenfeuchtA., HausslerD., & WarmuthM. (1986). Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension. Proceedings of the Eighteenth Annual Association for Computing Machinery Symposium on Theory of Computing (pp. 273?282). Berkeley, CA: Association for Computing Machinery.
  7. BradshawG. (1987). Learning about speech sounds: The NEXUS project. Proceedings of the Fourth International Workshop on Machine Learning (pp. 1?11). Irvine, CA: Morgan Kaufmann.
  8. BrooksL. (1978). Nonanalytic concept formation and memory for instances. In E.Rosch & B.B.Lloyd (Eds.), Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.
  9. BrooksL. (1989). Concept formation and particularizing learning. In S.Hanson & R.Davis (Eds.), Information, language, & cognition: Vancouver studies in cognitive science (Vol. 1). Vancouver, B.C.: UBC Press.
  10. CestnikB., KononenkoI., & BratkoI. (1987). ASSISTANT-86: A knowledge-elicitation tool for sophisticated users. In I.Bratko & N.Lavrac (Eds.), Progress in machine learning. Bled, Yugoslavia: Sigma Press.
  11. ClarkP.E. (1989). Exemplar-based reasoning in geological prospect appraisal (Technical Report 89?034). Glasgow, Scotland: Turing Institute.
  12. ClarkP.E., & NiblettT. (1989). The CN2 induction algorithm. Machine Learning, 3, 261?284.
  13. CoverT.M., & HartP.E. (1967). Nearest neighbor pattern classification. Institute of Electrical and Electronics Engineers Transactions on Information Theory, 13, 21?27. · DOI · Paper page
  14. DasarathyB.V. (1980). Nosing around the neighborhood: A new system structure and classification rule for recognition in partially exposed environments. Pattern Analysis and Machine Intelligence, 2, 67?71. · DOI
  15. DietterichT.G., & MichalskiR.S. (1983). A comparative review of selected methods for learning from examples. In R.S.Michalski, J.G.Carbonell, & T.M.Mitchell (Eds.), Machine learning: An artificial intelligence approach. San Mateo, CA: Morgan Kaufmann.
  16. FisherD.F. (1989). Noise-tolerant concept clustering. Proceedings of the Eleventh International Conference on Artificial Intelligence (pp. 825?830). Detroit, MI: Morgan Kaufmann.
  17. Gates, G.W. (1972). The reduced nearest neighbor rule. IEEE Transactions on Information Theory, 431?433. · DOI · Paper page
  18. HartP.E. (1968). The condensed nearest neighbor rule. Institute of Electrical and Electronics Engineers and Transactions on Information Theory, 14, 515?516. · DOI · Paper page
  19. HintzmanD.L. (1986). ?Schema abstraction? in a multiple-trace memory model. Psychological Review, 93, 411?428. · DOI · Paper page
  20. HoggR.V., & TanisE.A. (1983). Probability and statistical inference. New York, NY: Macmillan.
  21. Jabbour, K., Riveros, J.F.V., Landsbergen, D., & Meyer, W. (1987). ALFA: Automated load forecasting assistant. Proceedings of the 1987 IEEE Power Engineering Society Summer Meeting. San Francisco, CA.
  22. Kibler, D., & Aha, D.W. (1988). Case-based classification. Proceedings of the Case-Based Reasoning Workshop at AAAI 1988 (pp. 62?67). Unpublished manuscript.
  23. KiblerD., & AhaD.W. (1989). Comparing instance-saving with instance-averaging learning algorithms. In D.P.Benjamin (Ed.), Change of representation and inductive bias. Norwell, MA: Kluwer Academic Publishers.
  24. KiblerD., AhaD.W., & AlbertM. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, 5, 51?57. · DOI
  25. KotonP. (1988). Reasoning about evidence in causal explanations. Proceedings of the Seventh National Conference on Artificial Intelligence (pp. 256?261). St. Paul, MN: Morgan Kaufmann.
  26. MarkovitchS., & ScottP.D. (1989). Information filters and their implementation in the SYLLOG system. Proceedings of the Sixth International Workshop on Machine Learning (pp. 404?407). Ithaca, NY: Morgan Kaufmann. · DOI
  27. MichalskiR.S., & LarsonJ.B. (1978). Selection of most representative training examples and incremental generation of VL 1 hypotheses: The underlying methodology and the description of programs ESEL and AQ11 (Technical Report 867). Urbana, IL: University of Illinois, Department of Computer Science.
  28. MichalskiR.S., MozeticI., HongJ., & Lavra?N. (1986). The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 1041?1045). Philadelphia, PA: Morgan Kaufmann.
  29. Michie, D., Muggleton, S., Riese, C., & Zubrick, S. (1984). Rulemaster: A second-generation knowledge-engineering facility. 1984 Conference on Artificial Intelligence and Applications.
  30. NosofskyR.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 15, 39?57. · DOI · Paper page
  31. QuinlanJ.R. (1987). Generating production rules from decision trees. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 304?307). Milan, Italy: Morgan Kaufmann.
  32. QuinlanJ.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.
  33. Quinlan, J.R., Compton, P.J., Horn, K.A., & Lazurus, L. (1986). Inductive knowledge acquisition: A case study. Proceedings of the Second Australian Conference on Applications of Expert Systems. Sydney, Australia.
  34. RendellL. (1988). Learning hard concepts. Proceedings of the Third European Working Session on Learning (pp. 177?200). Glasgow, Scotland: Pitman Publishing.
  35. RisslandE.L., KolodnerJ., & WaltzD. (1989). Case-based reasoning from DARPA: Machine learning program plan. Proceedings of the Case-Based Reasoning Workshop (pp. 1?13). Pensecola Beach, FL: Morgan Kaufmann.
  36. RosenblattF. (1962). Principles of neurodynamics. New York, NY: Spartan.
  37. RumelhartD.E., McClellandJ.L., & The PDP Research Group (Eds.) (1987). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1). Cambridge, MA: MIT Press.
  38. Shamos, M.I., & Hoey, D. (1975). Closest point problems. Proceedings of the Sixteenth Annual Institute of Electrical and Electronic Engineers Symposium on the Foundations of Computer Science (pp. 151?162.) IEEE Computer Society. · DOI · Paper page
  39. SalzbergS. (1988). Exemplar-based learning: Theory and implementation (Technical Report TR-10?88). Cambridge, MA: Harvard University, Center for Research in Computing Technology.
  40. SchlimmerJ.C., & FisherD. (1986). A case study of incremental concept induction. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 496?501). Philadelphia, PA: Morgan Kaufmann.
  41. SmithE.E., & MedinD.L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press. · DOI · Paper page
  42. StanfillC., & WaltzD. (1986). Toward memory-based reasoning. Communications of the ACM, 29, 1213?1228. · DOI · Paper page
  43. UtgoffP.E. (1989). Incremental induction of decision trees. Machine Learning, 4, 161?186. · DOI · Paper page
  44. Van deVeldeW. (1989). IDL, or taming the multiplexer. Proceedings of the Fourth European Working Session on Learning (pp. 211?225). Montpellier, France: Morgan Kaufmann.
  45. VolperD.J., & HampsonS.E. (1987). Learning and using specific instances. Biological Cybernetics, 57, 57?71. · DOI

Cited by

Metadata

Authors
Aha, D. W., Kibler, D., & Albert, M. K.
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
1991-01
Volume
6
Issue
1
Pages
37-66
ISSN
  • 0885-6125
  • 1573-0565
DOI
10.1007/bf00153759
Cited by
2,163
References
5
Language
en
Licence
www.springer.com/tdm (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.