An experimental study on stability and generalization of extreme learning machines

This paper gives an experimental study on the stability of an extreme learning machine (ELM) and its generalization capability. Focusing on the relationship between uncertainty of an ELM’s output on the training set and the ELM’s generalization capability, the experiments show some new results in th...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 6; no. 1; pp. 129 - 135
Main Authors Fu, Aimin, Dong, Chunru, Wang, Laisheng
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2015
Springer Nature B.V
Subjects
Algorithms
Approximation
Artificial Intelligence
Artificial neural networks
Classification
Complex Systems
Computational Intelligence
Control
Engineering
Fuzzy sets
Machine learning
Mechatronics
Neural networks
Original Article
Pattern Recognition
Robotics
Stability
Systems Biology
Generalization capability
Uncertainty
Fuzziness
Extreme learning machine
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Summary:This paper gives an experimental study on the stability of an extreme learning machine (ELM) and its generalization capability. Focusing on the relationship between uncertainty of an ELM’s output on the training set and the ELM’s generalization capability, the experiments show some new results in the viewpoint of classical pattern recognition. The study provides some useful guidelines to choose a fraction of ELMs with better generalization from an ensemble for classification problems.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-014-0238-0