Robust learning algorithm for the mixture of experts

Hector Allende, Romina Torres, Rodrigo Salas, Claudio Moraga

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

The Mixture of Experts model (ME) is a type of modular artificial neural network (MANN) whose architecture is composed by different kinds of networks who compete to learn different aspects of the problem. This model is used when the searching space is stratified. The learning algorithm of the ME model consists in estimating the network parameters to achieve a desired performance. To estimate the parameters, some distributional assumptions are made, so the learning algorithm and, consequently, the parameters obtained depends on the distribution. But when the data is exposed to outliers the assumption is not longer valid, the model is affected and is very sensible to the data as it is showed in this work. We propose a robust learning estimator by means of the generalization of the maximum likelihood estimator called M-estimator. Finally a simulation study is shown, where the robust estimator presents a better performance than the maximum likelihood estimator (MLE).

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsFrancisco Jose Perales, Aurelio J. C. Campilho, Nicolas Perez Perez, Nicolas Perez Perez
PublisherSpringer Verlag
Pages19-27
Number of pages9
ISBN (Print)3540402179, 9783540402176
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2652
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Artificial Neural Networks
  • Mixtures of Experts
  • Robust Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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