TY - CHAP

T1 - Robust learning algorithm for the mixture of experts

AU - Allende, Hector

AU - Torres, Romina

AU - Salas, Rodrigo

AU - Moraga, Claudio

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2003

Y1 - 2003

N2 - 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).

AB - 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).

KW - Artificial Neural Networks

KW - Mixtures of Experts

KW - Robust Learning

UR - http://www.scopus.com/inward/record.url?scp=35248841469&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-44871-6_3

DO - 10.1007/978-3-540-44871-6_3

M3 - Chapter

AN - SCOPUS:35248841469

SN - 3540402179

SN - 9783540402176

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 19

EP - 27

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Perales, Francisco Jose

A2 - Campilho, Aurelio J. C.

A2 - Perez, Nicolas Perez

A2 - Perez, Nicolas Perez

PB - Springer Verlag

ER -