Elementary Features of Local Learning Algorithms
Locally Weighted Projection Regression (LWPR) and the learning classifier system XCSF share a common divide and conquer approach to approximate nonlinear function surfaces by means of three elementary features:
Clustering A complex non-linear problem is broken down into several smaller problems via kernels. The kernel structures are further optimized for accurate approximations.
KeywordsRoot Mean Square Error Local Model Input Space Radial Basis Function Network Elementary Feature
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer Fachmedien Wiesbaden 2014