Universal approximation using radial-basis-function networks
Neural Computation
Approximation by fully complex multilayer perceptrons
Neural Computation
Letters: Convex incremental extreme learning machine
Neurocomputing
Letters: Fully complex extreme learning machine
Neurocomputing
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Hinging hyperplanes for regression, classification, and function approximation
IEEE Transactions on Information Theory
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
Real-time learning capability of neural networks
IEEE Transactions on Neural Networks
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
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
A constructive enhancement for online sequential extreme learning machine
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
POFGEC: growing neural network of classifying potential function generators
International Journal of Knowledge Engineering and Soft Data Paradigms
Constructive approximation to multivariate function by decay RBF neural network
IEEE Transactions on Neural Networks
Two-stage extreme learning machine for regression
Neurocomputing
Incremental-based extreme learning machine algorithms for time-variant neural networks
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Ordinal extreme learning machine
Neurocomputing
Approximation capability of interpolation neural networks
Neurocomputing
The multidimensional function approximation based on constructive wavelet RBF neural network
Applied Soft Computing
ELM-Based time-variant neural networks with incremental number of output basis functions
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
Face recognition based on kernelized extreme learning machine
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Voting based extreme learning machine
Information Sciences: an International Journal
Efficient and effective algorithms for training single-hidden-layer 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
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
Modeling spectral data based on mutual information and kernel extreme learning machines
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Multi-class classification with one-against-one using probabilistic extreme learning machine
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Applying least angle regression to ELM
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
An incremental neural network with a reduced architecture
Neural Networks
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Weighted extreme learning machine for imbalance learning
Neurocomputing
Robust extreme learning machine
Neurocomputing
An online learning network for biometric scores fusion
Neurocomputing
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
Comparing studies of learning methods for human face gender recognition
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
A multi-objective micro genetic ELM algorithm
Neurocomputing
PCA-ELM: A Robust and Pruned Extreme Learning Machine Approach Based on Principal Component Analysis
Neural Processing Letters
Parallel Chaos Search Based Incremental Extreme Learning Machine
Neural Processing Letters
Extreme learning machine: a robust modeling technique? yes!
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Context-aware Discriminative Vocabulary Tree Learning for mobile landmark recognition
Digital Signal Processing
Meta-ELM: ELM with ELM hidden nodes
Neurocomputing
Clustering in extreme learning machine feature space
Neurocomputing
Hybrid extreme rotation forest
Neural Networks
Learning to Rank with Extreme Learning Machine
Neural Processing Letters
Applications of Hybrid Extreme Rotation Forests for image segmentation
International Journal of Hybrid Intelligent Systems
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Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892], which randomly generates hidden nodes and then analytically determines the output weights. Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] have proved in theory that although additive or RBF hidden nodes are generated randomly the network constructed by I-ELM can work as a universal approximator. During our recent study, it is found that some of the hidden nodes in such networks may play a very minor role in the network output and thus may eventually increase the network complexity. In order to avoid this issue and to obtain a more compact network architecture, this paper proposes an enhanced method for I-ELM (referred to as EI-ELM). At each learning step, several hidden nodes are randomly generated and among them the hidden node leading to the largest residual error decreasing will be added to the existing network and the output weight of the network will be calculated in a same simple way as in the original I-ELM. Generally speaking, the proposed enhanced I-ELM works for the widespread type of piecewise continuous hidden nodes.