The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.
Locally Weighted Learning
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Citations
Cites
Advances in neural information processing systems 7
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
Robust Locally Weighted Regression and Smoothing Scatterplots
Cleveland, W. S. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481038 · Cited by 11,594
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,520
Journal of The American Statistical Association
Fienberg, S. E. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1317 · Cited by 6,125
Hahn, G. J., Draper, N., & Smith, H. (1982) · Technometrics · doi:10.2307/1267833 · Cited by 5,773
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,742
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. S. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,708
A two-dimensional interpolation function for irregularly-spaced data
Shepard, D. (1968) · Proceedings of the 1968 23rd ACM national conference on - · doi:10.1145/800186.810616 · Cited by 5,079
A general regression neural network
Specht, D. F. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.97934 · Cited by 4,492
Cited by
Wettschereck, D., Aha, D. W., & Mohri, T. (1997) · Artificial Intelligence Review · doi:10.1023/a:1006593614256 · Cited by 717
Locally Weighted Learning for Control
Atkeson, C. G., Moore, A. W., & Schaal, S. (1997) · Artificial Intelligence Review · doi:10.1023/a:1006511328852 · Cited by 527
Self-citationRanking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results
Brazdil, P. B., Soares, C., & da Costa, J. P. (2003) · Machine Learning · doi:10.1023/a:1021713901879 · Cited by 406
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
Urain, J., Ginesi, M., Tateo, D., & Peters, J. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341035 · Cited by 56
Metadata
- Authors
- Atkeson, C. G., Moore, A. W., & Schaal, S.
- Venue
- Lazy Learning
- Publisher
- Springer Netherlands
- Published
- 1997
- Pages
- 11-73
- OpenAlex
- W4210969899
- Cited by
- 308
- References
- 117
- FWCI
- 26.65
- Type
- book chapter
- Language
- en
- Open access
- closed
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Locally Weighted Learning. https://app.cochat.ai/papers/10.1007/978-94-017-2053-3_2
- The paper
- Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally Weighted Learning. Lazy Learning. https://doi.org/10.1007/978-94-017-2053-3_2
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.
