Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning
Top Cited Papers
- 10 July 2009
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 20 (8), 1352-1357
- https://doi.org/10.1109/tnn.2009.2024147
Abstract
One of the open problems in neural network research is how to automatically determine network architectures for given applications. In this brief, we propose a simple and efficient approach to automatically determine the number of hidden nodes in generalized single-hidden-layer feedforward networks (SLFNs) which need not be neural alike. This approach referred to as error minimized extreme learning machine (EM-ELM) can add random hidden nodes to SLFNs one by one or group by group (with varying group size). During the growth of the networks, the output weights are updated incrementally. The convergence of this approach is proved in this brief as well. Simulation results demonstrate and verify that our new approach is much faster than other sequential/incremental/growing algorithms with good generalization performance.Keywords
This publication has 20 references indexed in Scilit:
- Improved extreme learning machine for function approximation by encoding a priori informationNeurocomputing, 2006
- Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden NodesIEEE Transactions on Neural Networks, 2006
- Fully complex extreme learning machineNeurocomputing, 2005
- Extreme learning machine: a new learning scheme of feedforward neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Approximation by Fully Complex Multilayer PerceptronsNeural Computation, 2003
- Approximation of multivariate functions using ridge polynomial networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Hinging hyperplanes for regression, classification, and function approximationIEEE Transactions on Information Theory, 1993
- The wavelet transform, time-frequency localization and signal analysisIEEE Transactions on Information Theory, 1990
- Orthonormal bases of compactly supported waveletsCommunications on Pure and Applied Mathematics, 1988
- Learning, invariance, and generalization in high-order neural networksApplied Optics, 1987