Neural networks for pattern recognition
Neural networks for pattern recognition
Ensemble modelling or selecting the best model: Many could be better than one
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Letters: Convex incremental extreme learning machine
Neurocomputing
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Patient Outcome Prediction with Heart Rate Variability and Vital Signs
Journal of Signal Processing Systems
Voting based extreme learning machine
Information Sciences: an International Journal
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Extreme learning machine (ELM) has shown its good performance in regression applications with a very fast speed. But there is still a difficulty to compromise between better generalization performance and smaller complexity of the ELM (a number of hidden nodes). This paper proposes a method called Delta Test-ELM (DT-ELM), which operates in an incremental way to create less complex ELM structures and determines the number of hidden nodes automatically. It uses Bayesian Information Criterion (BIC) as well as Delta Test (DT) to restrict the search as well as to consider the size of the network and prevent overfitting. Moreover, ensemble modeling is used on different DT-ELM models and it shows good test results in Experiments section.