Locally Weighted Learning

By Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal. 1997

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

Matching Networks for One Shot Learning (2016, background): “As discussed in Section 2, there are many links between content based attention, kernel based nearest neighbor and metric learning [1].”
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales (2005, background): “(The inner summation is familiar from work in locally-weighted 5 We discuss the ordinal regression variant in Section 6. learning 6 (Atkeson, Moore, and Schaal, 1997).)”
Learning Explainable Decision Rules via Maximum Satisfiability (2020, neutral): “learning approach for query-specific applications [17], [18].”
Exploiting Best-Match Equations for Efficient Reinforcement Learning (2011, methodology): “Like other lazy learning methods, for example, Atkeson et al. (1997), JIT Q-learning postpones updates until they are needed.”
Distance Metric Learning: A Comprehensive Survey (2006, background): “In addition, this paper discusses a number of techniques that is central to distance metric learning, including convex programming, positive semi-definite programming, kernel learning, dimension reduction, K Nearest Neighbor, large margin classification, and graph-based approaches.”
Fine-Grained Visual Comparisons with Local Learning (2014, methodology): “The basic idea in local learning [2, 6] is to concentrate the learning algorithm on training instances that are most similar to the test example.”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, methodology): “Regression methods, such as neural networks, Gaussian processes, and (weighted) linear regressions, are commonly used for learning transition models [334, 237, 83, 15].”
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning (2002, background): “LWL provides a principled approach to learning models of complex phenomena, dealing with large amounts of data, training quickly, and avoiding interference between multiple tasks during control of complex systems [11, 12].”
Machine learning approaches for estimation of prediction interval for the model output (2006, unknown): “More details about locally weighted regression can be found in Atkeson, Moore, and Schaal (1997).”
A Survey of Collectives (2004, background): “Adaptive control [7, 196], and in particular adaptive control involving locally weighted RL algorithms [9, 165], constitute a broadly applicable framework for controlling small, potentially inexactly modeled systems.”
Multinomial Naive Bayes for Text Categorization Revisited (2004, background): “In this section we discuss how MNB can be improved further using locally weighted learning [12].”