Letters: Convex incremental extreme learning machine
Neurocomputing
A Robust Online Sequential Extreme Learning Machine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
An Improved On-Line Sequential Learning Algorithm for Extreme Learning Machine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Active Noise Control Using a Feedforward Network with Online Sequential Extreme Learning Machine
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Small Number of Hidden Units for ELM with Two-Stage Linear Model
IEICE - Transactions on Information and Systems
An Online Self-constructing Fuzzy Neural Network with Restrictive Growth
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A Random Network Ensemble for Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Real-Time Collaborative Filtering Using Extreme Learning Machine
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Evolving logic networks with real-valued inputs for fast incremental learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
A constructive enhancement for online sequential extreme learning machine
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel reformulated radial basis function neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Online adaptive radial basis function networks for robust object tracking
Computer Vision and Image Understanding
Online training for single hidden-layer Online training for single hidden-layer
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A new online learning algorithm for structure-adjustable extreme learning machine
Computers & Mathematics with Applications
Realtime training on mobile devices for face recognition applications
Pattern Recognition
Fault prognosis of mechanical components using on-line learning neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Ordinal extreme learning machine
Neurocomputing
Error tolerance based support vector machine for regression
Neurocomputing
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network
Neural Processing Letters
Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks
Pattern Recognition Letters
Enhanced extreme learning machine with modified gram-schmidt algorithm
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Incremental face recognition for large-scale social network services
Pattern Recognition
On-Line extreme learning machine for training time-varying neural networks
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Novel weighting in single hidden layer feedforward neural networks for data classification
Computers & Mathematics with Applications
A retrieval method adaptively reducing user's subjective impression gap
Multimedia Tools and Applications
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
A rank reduced matrix method in extreme learning machine
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
An online learning network for biometric scores fusion
Neurocomputing
Extreme learning machines for intrusion detection systems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
A multi-objective micro genetic ELM algorithm
Neurocomputing
Parallel Chaos Search Based Incremental Extreme Learning Machine
Neural Processing Letters
FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds
Computers in Biology and Medicine
Clustering in extreme learning machine feature space
Neurocomputing
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
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In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance