Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes
Neural Processing Letters
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
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
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Pruning error minimization in least squares support vector machines
IEEE Transactions on Neural Networks
An improved conjugate gradient scheme to the solution of least squares SVM
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
SMO-based pruning methods for sparse least squares support vector machines
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
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We introduce a fast sparse approximation schemes of extreme learning machine (ELM) named FSA-ELM of extreme learning machine (ELM). Our algorithms have two compelling features: low complexity and sparse solution. Experiments on benchmark data sets show that the proposed algorithm obtains sparse classifiers at a rather low complexity without sacrificing the generalization performance. As validated by the simulation results, FSA-ELM tends to have better scalability and achieves similar or much better generalization performance with much faster learning speed than the traditional ELM algorithm.