Locally Weighted Learning

Article by Atkeson, Moore and Schaal (1997)

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Locally Weighted Learning

Summary

Locally Weighted Learning is an academic journal article^[1].

Key Facts

Locally Weighted Learning authored Christopher G. Atkeson^[2].
Locally Weighted Learning authored Andrew William Moore^[3].
Locally Weighted Learning authored Stefan Schaal^[4].
Locally Weighted Learning's instance of is recorded as academic journal article^[5].
Locally Weighted Learning's followed by is recorded as Locally Weighted Learning for Control^[6].
Locally Weighted Learning's DOI is recorded as 10.1023/A:1006559212014^[7].
Locally Weighted Learning's publication date is recorded as +1997-02-00T00:00:00Z^[8].
Locally Weighted Learning's main subject is recorded as machine learning^[9].
Locally Weighted Learning's number of pages is recorded as {'unit': '1', 'amount': '+63'}^[10].
Locally Weighted Learning's published in is recorded as Artificial Intelligence Review^[11].
Locally Weighted Learning's title is recorded as Locally Weighted Learning^[12].
Locally Weighted Learning's cites work is recorded as Ninth National Conference on Artificial Intelligence^[13].
Locally Weighted Learning's cites work is recorded as An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression^[14].
Locally Weighted Learning's cites work is recorded as Using Local Models to Control Movement^[15].
Locally Weighted Learning's cites work is recorded as Memory-based neural networks for robot learning^[16].
Locally Weighted Learning's cites work is recorded as Q56638869^[17].
Locally Weighted Learning's cites work is recorded as Local Learning Algorithms^[18].
Locally Weighted Learning's cites work is recorded as Surface Learning with Applications to Lipreading^[19].
Locally Weighted Learning's cites work is recorded as Robust Locally Weighted Regression and Smoothing Scatterplots^[20].
Locally Weighted Learning's cites work is recorded as Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting^[21].
Locally Weighted Learning's cites work is recorded as Local Regression Models^[22].
Locally Weighted Learning's cites work is recorded as Smoothing by Local Regression: Principles and Methods^[23].
Locally Weighted Learning's cites work is recorded as A weighted nearest neighbor algorithm for learning with symbolic features^[24].
Locally Weighted Learning's cites work is recorded as Advances in Neural Information Processing Systems 6^[25].
Locally Weighted Learning's cites work is recorded as Memory-Based Methods for Regression and Classification^[26].

Body

Designation and Status

Locally Weighted Learning's instance of is recorded as academic journal article^[5].

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Quick Facts

Instance of academic journal article

Author Christopher G. Atkeson, Andrew William Moore, Stefan Schaal

Publication date February 1997

Followed by Locally Weighted Learning for Control

Properties

Cites work Ninth National Conference on Artificial Intelligence, An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression, Using Local Models to Control Movement, Memory-based neural networks for robot learning, Q56638869, Local Learning Algorithms, Surface Learning with Applications to Lipreading, Robust Locally Weighted Regression and Smoothing Scatterplots, Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, Local Regression Models, Smoothing by Local Regression: Principles and Methods, A weighted nearest neighbor algorithm for learning with symbolic features, Advances in Neural Information Processing Systems 6, Memory-Based Methods for Regression and Classification, Applied Regression Analysis, Non-Parametric Estimation of a Multivariate Probability Density, Local Linear Regression Smoothers and Their Minimax Efficiencies, Variable Bandwidth and Local Linear Regression Smoothers, Data-Driven Bandwidth Selection in Local Polynomial Fitting: Variable Bandwidth and Spatial Adaptation, Local Polynomial Modelling and Its Applications, Predicting chaotic time series, An Algorithm for Finding Best Matches in Logarithmic Expected Time, Q135982920, Image registration by local approximation methods, Local Regression: Automatic Kernel Carpentry, Generalized Additive Models, Discriminant Adaptive Nearest Neighbor Classification and Regression, Linear and nonlinear methods of nonparametric regression analysis, Instance-based prediction of real-valued attributes, Context and structure in conceptual combination, Memory-based Stochastic Optimization, On Estimating Regression, Rules and exemplars in categorization, identification, and recognition, Bumptrees for Efficient Function, Constraint and Classification Learning, Selecting the smoothing parameter for estimation of slowly changing evoked potential signals, Multivariate interpolation of large sets of scattered data, Multivariate Locally Weighted Least Squares Regression, Distribution-Free Methods of Approximating Nonlinear Marketing Relationships, Assessing the Quality of Learned Local Models, From Isolation to Cooperation: An Alternative View of a System of Experts, A general regression neural network, Toward memory-based reasoning, Consistent Nonparametric Regression, Optimal Rates of Convergence for Nonparametric Estimators, Optimal Global Rates of Convergence for Nonparametric Regression, Pattern recognition by means of automatic analogue apparatus, Is Learning The n-th Thing Any Easier Than Learning The First?, Local Likelihood Estimation, Exploratory Data Analysis, Estimation of trial-to-trial variation in evoked potential signals by smoothing across trials, Principles of Risk Minimization for Learning Theory, Local Algorithms for Pattern Recognition and Dependencies Estimation, Smooth Regression Analysis, Multivariate Density Estimation

Imported from wholeness_audit_2026-05-02

Main subject machine learning

Number of pages unit: 1, amount: +63

Published in Artificial Intelligence Review

Title Locally Weighted Learning

External References (4)

Doi 10.1023/A:1006559212014

Google scholar paper id 4965452880008676588

Semantic scholar corpus id 9219592

Semantic scholar paper id 6b502694a8e23d3b9ab08fa7a0c6d5e82bd1f066

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

[5] ↑ Locally Weighted Learning — instance of (P31): academic journal article. wikidata.org.
[2] ↑ Locally Weighted Learning — author (P50): Christopher G. Atkeson. DOI.org query endpoint. wikidata.org.
[3] ↑ Locally Weighted Learning — author (P50): Andrew William Moore. DOI.org query endpoint. wikidata.org.
[4] ↑ Locally Weighted Learning — author (P50): Stefan Schaal. DOI.org query endpoint. wikidata.org.
[6] ↑ Locally Weighted Learning — followed by (P156): Locally Weighted Learning for Control. wikidata.org.
[7] ↑ Locally Weighted Learning — DOI (P356): 10.1023/A:1006559212014. wikidata.org.
[8] ↑ Locally Weighted Learning — publication date (P577): +1997-02-00T00:00:00Z. DOI.org query endpoint. wikidata.org.
[9] ↑ Locally Weighted Learning — main subject (P921): machine learning. wikidata.org.
[10] ↑ Locally Weighted Learning — number of pages (P1104): {'unit': '1', 'amount': '+63'}. wikidata.org.
[11] ↑ Locally Weighted Learning — published in (P1433): Artificial Intelligence Review. DOI.org query endpoint. wikidata.org.
[12] ↑ Locally Weighted Learning — title (P1476): Locally Weighted Learning. wikidata.org.
[13] ↑ Locally Weighted Learning — cites work (P2860): Ninth National Conference on Artificial Intelligence. DOI.org query endpoint. wikidata.org.
[14] ↑ Locally Weighted Learning — cites work (P2860): An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. DOI.org query endpoint. wikidata.org.
[15] ↑ Locally Weighted Learning — cites work (P2860): Using Local Models to Control Movement. DOI.org query endpoint. wikidata.org.
[16] ↑ Locally Weighted Learning — cites work (P2860): Memory-based neural networks for robot learning. DOI.org query endpoint. wikidata.org.
[17] ↑ Locally Weighted Learning — cites work (P2860): Q56638869. DOI.org query endpoint. wikidata.org.
[18] ↑ Locally Weighted Learning — cites work (P2860): Local Learning Algorithms. DOI.org query endpoint. wikidata.org.
[19] ↑ Locally Weighted Learning — cites work (P2860): Surface Learning with Applications to Lipreading. DOI.org query endpoint. wikidata.org.
[20] ↑ Locally Weighted Learning — cites work (P2860): Robust Locally Weighted Regression and Smoothing Scatterplots. DOI.org query endpoint. wikidata.org.
[21] ↑ Locally Weighted Learning — cites work (P2860): Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. DOI.org query endpoint. wikidata.org.
[22] ↑ Locally Weighted Learning — cites work (P2860): Local Regression Models. DOI.org query endpoint. wikidata.org.
[23] ↑ Locally Weighted Learning — cites work (P2860): Smoothing by Local Regression: Principles and Methods. DOI.org query endpoint. wikidata.org.
[24] ↑ Locally Weighted Learning — cites work (P2860): A weighted nearest neighbor algorithm for learning with symbolic features. DOI.org query endpoint. wikidata.org.
[25] ↑ Locally Weighted Learning — cites work (P2860): Advances in Neural Information Processing Systems 6. DOI.org query endpoint. wikidata.org.
[26] ↑ Locally Weighted Learning — cites work (P2860): Memory-Based Methods for Regression and Classification. DOI.org query endpoint. wikidata.org.

Class ancestry

[1] ↑ Locally Weighted Learning is an instance of academic journal article (Q18918145) [P31, primary]. Wikidata. wikidata.org.

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@misc{4ortxyz_locally-weighted-learning_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Locally Weighted Learning}}, year = {2026}, url = {https://4ort.xyz/entity/locally-weighted-learning}, note = {Accessed: 2026-05-03}}

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