Journal ArticleDOI
Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning
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TLDR
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 which is much faster than other sequential/incremental/growing algorithms with good generalization performance.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.read more
Citations
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Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal ArticleDOI
Extreme learning machines: a survey
TL;DR: A survey on Extreme learning machine (ELM) and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELm, (3) online sequential ELM; and (4) incremental ELM and (5) ensemble ofELM.
Journal ArticleDOI
Trends in extreme learning machines
TL;DR: In this paper, the authors report the current state of the theoretical research and practical advances on this subject and provide a comprehensive view of these advances in ELM together with its future perspectives.
Journal ArticleDOI
Optimization method based extreme learning machine for classification
TL;DR: Under the ELM learning framework, SVM's maximal margin property and the minimal norm of weights theory of feedforward neural networks are actually consistent and ELM for classification tends to achieve better generalization performance than traditional SVM.
Journal ArticleDOI
Extreme learning machine: algorithm, theory and applications
TL;DR: This paper describes the latest progress of ELM in recent years, including the model and specific applications of ELm, and finally points out the research and development prospects ofELM in the future.
References
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Book
Linear Matrix Inequalities in System and Control Theory
TL;DR: In this paper, the authors present a brief history of LMIs in control theory and discuss some of the standard problems involved in LMIs, such as linear matrix inequalities, linear differential inequalities, and matrix problems with analytic solutions.
Journal ArticleDOI
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
Journal ArticleDOI
Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.