The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Negative correlation in incremental learning
Natural Computing: an international journal
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Ordinal extreme learning machine
Neurocomputing
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Voting based extreme learning machine
Information Sciences: an International Journal
Incremental face recognition for large-scale social network services
Pattern Recognition
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
International Journal of Automation and Computing
Displacement prediction model of landslide based on ensemble of extreme learning machine
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
A multi-objective micro genetic ELM algorithm
Neurocomputing
A study on the randomness reduction effect of extreme learning machine with ridge regression
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Meta-ELM: ELM with ELM hidden nodes
Neurocomputing
Quantifying the reliability of fault classifiers
Information Sciences: an International Journal
Hybrid extreme rotation forest
Neural Networks
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Liang et al. [A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006), 1411-1423] has proposed an online sequential learning algorithm called online sequential extreme learning machine (OS-ELM), which can learn the data one-by-one or chunk-by-chunk with fixed or varying chunk size. It has been shown [Liang et al., A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006) 1411-1423] that OS-ELM runs much faster and provides better generalization performance than other popular sequential learning algorithms. However, we find that the stability of OS-ELM can be further improved. In this paper, we propose an ensemble of online sequential extreme learning machine (EOS-ELM) based on OS-ELM. The results show that EOS-ELM is more stable and accurate than the original OS-ELM.