Consistency of multilayer perceptron regression estimators

Authors:
Jan Mielniczuk;Joanna Tyrcha
Affiliations:
Institute of Computer Science, Poland;Institute of Computer Science, Poland
Venue:
Neural Networks
Year:
1993

Citing 3
Cited 3

What size net gives valid generalization?

Neural Computation
Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings

Neural Networks
Generalization Performance of Overtrained Back-Propagation Networks

Proceedings of the EURASIP Workshop 1990 on Neural Networks

PAGER: parameterless, accurate, generic, efficient kNN-based regression

DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Analysis of the rate of convergence of least squares neural network regression estimates in case of measurement errors

Neural Networks
Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM Algorithm for Classification

International Journal of Applied Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the paper three layer perceptron with one hidden layer and the output layer consisting of one neuron is considered. This is commonly used architecture to solve regression problems where such a perceptron minimizing the mean squared error criterion for the data points (x"k, y"k), k = 1,..., N is sought. It is shown that in the model: y"k = g"0(x"k) + @ek, k = 1, ... N, where x"k is independent from zero mean error term @e"k, this procedure is consistent when N - ~, provided that g"0 is represented as three layer perceptron with Heaviside transfer fucntion. The same result is true when transfer function is an arbitrary continuous function with bounded limits at +/-~ and the hidden-to-output weights in the considered family of perceptions are bounded.