Multi layer multi objective extreme learning machine
| Autor: | Guang-Bin Huang, Chamara Kasun Liyanaarachchi Lekamalage, Dongshun Cui, Kang Song, Ken Liang |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Contextual image classification Artificial neural network Linear programming Computer science business.industry Pattern recognition 02 engineering and technology symbols.namesake 020901 industrial engineering & automation Boltzmann constant 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Artificial intelligence business Multi layer Extreme learning machine Network model |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2017.8296491 |
| Popis: | 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 (MLELM) 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. |
| Databáze: | OpenAIRE |
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