On model selection in SLT and linear basis neural networks

Author

Aguirre, Arturo Hernández ; Koutsougeras, Cris ; Buckles, Bill P.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA, USA

Volume

1

fYear

1999

fDate

1999

Firstpage

467

Abstract

This paper presents an approach to the experimental verification of the quality of “model selection” delivered by statistical learning theory (SLT). We depart from a function whose analytical approximation properties by polynomials are well known and readily verifiables in our experimental environment. For different sample size sets, the model predicted by SLT is contrasted against the model derived from the mathematical properties of the function. We found great robustness in the predictive ability of SLT

Keywords

function approximation; learning (artificial intelligence); least squares approximations; neural nets; polynomials; statistical analysis; analytical approximation; least squares approximation; linear basis neural networks; model selection; polynomials; statistical learning theory; Approximation error; Chebyshev approximation; Function approximation; Intelligent networks; Machine learning; Mathematical model; Neural networks; Polynomials; Predictive models; Statistical learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831540

Filename

831540