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2016 | OriginalPaper | Buchkapitel

Negative Correlation Learning with Difference Learning

verfasst von : Yong Liu

Erschienen in: Computational Intelligence and Intelligent Systems

Verlag: Springer Singapore

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Abstract

In order to learn a given data set, a learning system often has to learn too much on some data points in the given data set in order to learn well the rest of the given data. Such unnecessary learning might lead to both the higher complexity and overfitting in the learning system. In order to control the complexity of neural network ensembles, difference learning is introduced into negative correlation learning. The idea of difference learning is to let each individual in an ensemble learn to be different to the ensemble on some selected data points when the outputs of the ensemble are too close to the target values of these data points. It has been found that such difference learning could control not only overfitting in an ensemble, but also weakness among the individuals in the ensemble. Experimental results were conducted to show how such difference learning could create rather weak learners in negative correlation learning.

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Vorheriges Kapitel The Comparisons Between the Improved Numerical Mode-Matching Method (NMM) and the Traditional NMM Using for Resistivity Logging

Nächstes Kapitel SURF Feature Description of Color Image Based on Gaussian Model

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Titel: Negative Correlation Learning with Difference Learning
verfasst von: Yong Liu
Verlag: Springer Singapore
Buch: Computational Intelligence and Intelligent Systems
Print ISBN: 978-981-10-0355-4

Electronic ISBN: 978-981-10-0356-1

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-981-10-0356-1_27

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