Machine Learning
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Convex incremental extreme learning machine
Neurocomputing
NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
The Journal of Machine Learning Research
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
Letters: Fully complex extreme learning machine
Neurocomputing
Extreme support vector machine classifier
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
IEEE Transactions on Information Theory
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
Face recognition based on kernelized extreme learning machine
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Learning Topographic Representations of Nature Images with Pairwise Cumulant
Neural Processing Letters
Information Sciences: an International Journal
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
Research of dynamic load identification based on extreme learning machine
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Weighted extreme learning machine for imbalance learning
Neurocomputing
Editorial: Advances in Extreme Learning Machines (ELM2011)
Neurocomputing
Engineering Applications of Artificial Intelligence
Boosting weighted ELM for imbalanced learning
Neurocomputing
Clustering in extreme learning machine feature space
Neurocomputing
Learning to Rank with Extreme Learning Machine
Neural Processing Letters
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Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset (and/or multi-label) classification applications. The ELM theory shows that the hidden nodes of the ''generalized'' single-hidden layer feedforward networks (SLFNs), which need not be neuron alike, can be randomly generated and the universal approximation capability of such SLFNs can be guaranteed. This paper further studies ELM for classification in the aspect of the standard optimization method and extends ELM to a specific type of ''generalized'' SLFNs-support vector network. This paper shows that: (1) under the ELM learning framework, SVM's maximal margin property and the minimal norm of weights theory of feedforward neural networks are actually consistent; (2) from the standard optimization method point of view ELM for classification and SVM are equivalent but ELM has less optimization constraints due to its special separability feature; (3) as analyzed in theory and further verified by the simulation results, ELM for classification tends to achieve better generalization performance than traditional SVM. ELM for classification is less sensitive to user specified parameters and can be implemented easily.