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.


Root 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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Copyright information

© Springer Fachmedien Wiesbaden 2014

Authors and Affiliations

  1. 1.Lehrstuhl für kognitive ModellierungUniversität TübingenTübingenGermany

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