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Ensembling Extreme Learning Machines
2007Extreme learning machine (ELM) is a novel learning algorithm much faster than the traditional gradient-based learning algorithms for single-hidden-layer feedforward neural networks (SLFNs). Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem.
Huawei Chen +3 more
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Multiple kernel extreme learning machine
Neurocomputing, 2015Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of ...
Xinwang Liu +2 more
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Learning from correlation with extreme learning machine
International Journal of Machine Learning and Cybernetics, 2019A seemingly unrelated regression (SUR) refers to several individual equations among which there is not an explicit connection such as one equation’s observation is another equation’s response, but there exists an implicit relation represented by correlated disturbances of response variables.
Li Zhao, Jie Zhu
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Dimension Reduction With Extreme Learning Machine
IEEE Transactions on Image Processing, 2016Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or ...
Liyanaarachchi Lekamalage Chamara Kasun +3 more
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Convex incremental extreme learning machine
Neurocomputing, 200710.1016/j.neucom.2007.02.009 ; Neurocomputing ; 70 ; 16-18 ; 3056-3062 ...
Guang-Bin Huang, Lei Chen 0009
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Dolphin Swarm Extreme Learning Machine
Cognitive Computation, 2017As a novel learning algorithm for a single hidden-layer feedforward neural network, the extreme learning machine has attracted much research attention for its fast training speed and good generalization performances. Instead of iteratively tuning the parameters, the extreme machine can be seen as a linear optimization problem by randomly generating the
Tianqi Wu, Min Yao, Jianhua Yang
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Advances in Extreme Learning Machines (ELM2011)
Neurocomputing, 2013Abstract is not available in fulltext.
Guang-Bin Huang, Dianhui Wang 0001
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2018
<p>Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights and biases are generated randomly and never change. The method in general achieves performance comparable to Error Back-Propagation, but the training time is
Anton Akusok +6 more
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<p>Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights and biases are generated randomly and never change. The method in general achieves performance comparable to Error Back-Propagation, but the training time is
Anton Akusok +6 more
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Genetically optimized extreme learning machine
2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), 2013This paper proposes a learning algorithm for single-hidden layer feedforward neural networks (SLFN) called genetically optimized extreme learning machine (GO-ELM). In the GO-ELM, the structure and the parameters of the SLFN are optimized by a genetic algorithm (GA).
Tiago Matias +3 more
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Extreme Learning Machines with Simple Cascades
Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 2015We compare extreme learning machines with cascade correlation on a standard benchmark dataset for comparing cascade networks along with another commonly used dataset. We introduce a number of hybrid cascade extreme learning machine topologies ranging from simple shallow cascade ELM networks to full cascade ELM networks.
Tom Gedeon, Anthony Oakden
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