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Multi Layer Multi Objective Extreme Learning Machine

Citation Author(s):
Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang
Submitted by:
Chamara Liyanaa...
Last updated:
12 September 2017 - 11:22pm
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters:
Cui Dongshun
Paper Code:
1990
 

Fully connected multi layer neural networks such as Deep Boltzmann Machines (DBM) performs better than fully connected single layer neural networks in image classification tasks and has a smaller number of hidden layer neurons than Extreme Learning Machine (ELM) based fully connected multi layer neural networks such as Multi Layer ELM (ML-ELM) and Hierarchical ELM (H-ELM) However, ML-ELM and H-ELM has a smaller training time than DBM. This paper introduces a fully connected multi layer neural network referred to as Multi Layer Multi Objective Extreme Learning Machine (MLMO-ELM) which uses a multi objective formulation to pass the label and non-linear information in order to learn a network model which has a similar number of hidden layer parameters as DBM and smaller training time than DBM. The experimental results show that MLMO-ELM outperforms DBM, ML-ELM and H-ELM on OCR and NORB datasets.

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