首页|Nonparametric Estimation of Edge Values of Regression Functions
Nonparametric Estimation of Edge Values of Regression Functions
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NETL
Springer Nature
In this article we investigate the problem of regression functions estimation in the edges points of their domain。 We refer to the model y_i = R (x_i) + ε_i， i = 1， 2，。。。 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(。) is a completely unknown function。 The possible solution of finding unknown function is to apply the algorithms based on the Parzen kernel。 The commonly known drawback of these algorithms is that the error of estimation dramatically increases if the point of estimation x is drifting to the left or right bound of interval D。 This fact makes it impossible to estimate functions exactly in edge values of domain。 The main goal of this paper is an application of NMS algorithm (introduced in)， basing on integral version of the Parzen method of function estimation by combining the linear approximation idea。 The results of numerical experiments are presented。
Institute of Computational Intelligence, Czestochowa University of Technology, Czestochowa, Poland
Information Technology Institute, University of Social Sciences, Lodz, Poland,Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada
International conference on artificial intelligence and soft computing
NETL
Springer Nature
