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A Detailed Analysis on Extreme Learning Machine and Novel Approaches Based on ELM
Current Issue
Volume 1, 2014
Issue 5 (November)
Pages: 43-50   |   Vol. 1, No. 5, November 2014   |   Follow on         
Paper in PDF Downloads: 75   Since Aug. 28, 2015 Views: 2371   Since Aug. 28, 2015
Authors
[1]
Ömer Faruk Ertuğrul, Dept. Electrical and Electronic Engineering, Batman University, Batman, Turkey.
[2]
Yılmaz Kaya, Dept. Computer Engineering, Siirt University, Siirt, Turkey.
Abstract
Extreme learning machine (ELM) is a train method for single hidden layer feed forward neural network. The input weights and biases of ELM are selected randomly and output weights are determined analytically therefore ELM has a fast train stage. Unfortunately randomly selection of input weights and biases causes unstable accurate results. Accuracy of randomly selected input weights and biases (ELM) was compared with new proposed approaches: predefined input weights and biases (ELM-P) and determining input weights and biases by back propagation (ELM-B). Also novel approaches; single layer ELM (sELM), tuning ELM (tELM), ELM based on linear regression (ELMr) were proposed for determining the output weights instead of using Moore–Penrose generalized inverse method. The accuracies of proposed approaches were compared with each other and ELM. The results were showed that the proposed approaches are successful.
Keywords
Extreme Learning Machine, Backpropagation, Single Layer, Tuning, Linear Regression, Stability, Predefined Weights
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