论文信息 - A Comparative Study of Extreme Learning Machine Pruning Based on Detection of Linear Independence

A Comparative Study of Extreme Learning Machine Pruning Based on Detection of Linear Independence

Extreme Learning Machine (ELM) is gaining fairly popularity in training neural networks, due to its simplicity and speed. However, the number of neurons in the hidden layer is still an open problem. This paper proposes a method for pruning the hidden layer neurons based on the linear combination of the hidden layer weights and the input data and compare four methods of detecting linear dependence between vectors.

[1] Dianhui Wang,et al. Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[2] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[3] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[4] A. R. L. Oliveira,et al. Detecção de Linhas Redundantes em Problemas de Programação Linear de Grande Porte , 2011 .

[5] A. Asuncion,et al. UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[6] Audra E. Kosh,et al. Linear Algebra and its Applications , 1992 .

[7] Guang-Bin Huang,et al. Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).