The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Constructive Incremental Learning from Only Local Information
Abstract
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model, as well as the parameters of the locally linear model itself, are learned independently, that is, without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross-validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness toward negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system that profits from combining independent expert knowledge on the same problem. This article illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.
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Cites
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Cited by
A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227
Learning of action through adaptive combination of motor primitives
Thoroughman, K. A., & Shadmehr, R. (2000) · Nature · doi:10.1038/35037588 · Cited by 945
Perspectives and problems in motor learning
Wolpert, D. M., Ghahramani, Z., & Flanagan, J. R. (2001) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01773-3 · Cited by 667
Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics
Schaal, S. (2006) · Adaptive Motion of Animals and Machines · doi:10.1007/4-431-31381-8_23 · Cited by 599
Self-citationIncremental Online Learning in High Dimensions
Vijayakumar, S., D'Souza, A., & Schaal, S. (2005) · Neural Computation · doi:10.1162/089976605774320557 · Cited by 527
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Metadata
- Authors
- Schaal, S., & Atkeson, C. G.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1998-11-01
- Volume
- 10
- Issue
- 8
- Pages
- 2047-2084
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2167804690
- Cited by
- 517
- References
- 85
- FWCI
- 15.75
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
