Locally Weighted Regression Algorithm

From GM-RKB
Jump to: navigation, search

A Locally Weighted Regression (LWR) Algorithm is a machine learning regression algorithm that is based on locally weighted learning.



References

2017a

2017b

  • https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/cohn96a-html/node7.html
    • QUOTE: Model-based methods, such as neural networks and the mixture of Gaussians, use the data to build a parameterized model. After training, the model is used for predictions and the data are generally discarded. In contrast, "memory-based" methods are non-parametric approaches that explicitly retain the training data, and use it each time a prediction needs to be made. Locally weighted regression (LWR) is a memory-based method that performs a regression around a point of interest using only training data that are “local to that point. One recent study demonstrated that LWR was suitable for real-time control by constructing an LWR-based system that learned a difficult juggling task [Schaal & Atkeson 1994]. ...

1997a

1997b

  • (Mitchell, 1997) ⇒ Tom M. Mitchell. (1997). “Machine Learning." McGraw-Hill.
    • QUOTE: Section 8.6 Remarks on Lazy and Eager Learning: In this chapter we considered three lazy learning methods: the k-Nearest Neighbor algorithm, locally weighted regression, and case-based reasoning.