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Kernel Estimation of Regression Functions in the Boundary Regions

  • Tomasz Gałkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7895)

Abstract

The article refers to the problem of regression functions estimation in the points near the edges of their domain. We investigate the model \(y_i = R\left( {x_i } \right) + \epsilon _i ,\,i = 1,2, \ldots n\), where x i is assumed to be the set of deterministic inputs, x i  ∈ D, y i is the set of probabilistic outputs, and ε i is a measurement noise with zero mean and bounded variance. \(R\left( . \right)\) is a completely unknown function. The possible clue of finding unknown function is to apply the algorithms based on Parzen kernel [5], [12]. The commonly known inconvenience of these algorithms is that the error of estimation dramatically increases if the point of estimation x is coming up to the left or right bound of interval D.

The main result of this paper is a new, original algorithm (named NMS) based on integral version of Parzen methods for estimation of edge values of a function R. The cross-validation-like technique is used in this procedure. The results of numerical experiments are presented.

Keywords

IEEE Transaction Regression Function Kernel Estimation Orthogonal Series Multiple Fourier Series 
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 2013

Authors and Affiliations

  • Tomasz Gałkowski
    • 1
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzestochowaPoland

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