Pattern classification based on local learning

  • Jing Peng
  • Bir Bhanu
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


Local learning methods approximate a target function (a posteriori probability) by partitioning the input space into a set of local regions, and modeling a simple input-output relationship in each one. In order for local learning to be effective for pattern classification in high dimensional settings, regions must be chosen judiciously to minimize bias. This paper presents a novel region partitioning criterion that attempts to minimize bias by capturing differential relevance in input variables in an efficient way. The efficacy of the method is validated using a variety of real and simulated data.


Input Space Target Function Pattern Classification Split Point Local Learning 
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-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jing Peng
    • 1
  • Bir Bhanu
    • 1
  1. 1.College of EngineeringUniversity of CaliforniaRiversideUSA

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