Matrix computations (3rd ed.)
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Alpha seeding for support vector machines
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications
Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
An incremental training method for the probabilistic RBF network
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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The single-hidden-layer feedforward neural networks (SLFNs) are frequently used in machine learning due to their ability which can form boundaries with arbitrary shapes if the activation function of hidden units is chosen properly. Most learning algorithms for the neural networks based on gradient descent are still slow because of the many learning steps. Recently, a learning algorithm called extreme learning machine (ELM) has been proposed for training SLFNs to overcome this problem. It randomly chooses the input weights and hidden-layer biases, and analytically determines the output weights by the matrix inverse operation. This algorithm can achieve good generalization performance with high learning speed in many applications. However, this algorithm often requires a large number of hidden units and takes long time for classification of new observations. In this paper, a new approach for training SLFNs called least-squares extreme learning machine (LS-ELM) is proposed. Unlike the gradient descent-based algorithms and the ELM, our approach analytically determines the input weights, hidden-layer biases and output weights based on linear models. For training with a large number of input patterns, an online training scheme with sub-blocks of the training set is also introduced. Experimental results for real applications show that our proposed algorithm offers high classification accuracy with a smaller number of hidden units and extremely high speed in both learning and testing.