Hybrid meta-heuristic algorithm based parameter optimization for extreme learning machines classification
Most classification algorithms suffer from manual parameter tuning and it affects the training computational time and accuracy performance. Extreme Learning Machines (ELM) emerged as a fast training machine learning algorithm that eliminates parameter tuning by randomly assigning the input weights a...
Saved in:
Main Author: | Alade, Oyekale Abel |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/101549/1/OyekaleAbelAladePSC2021.pdf http://eprints.utm.my/id/eprint/101549/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150572 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing extreme learning machines classification with moth-flame optimization technique
by: Alade, Oyekale Abel, et al.
Published: (2022) -
Enhancing extreme learning machines using cross-entropy moth-flame optimization algorithm
by: Alade, Oyekale Abel, et al.
Published: (2022) -
Time series predictive analysis based on hybridization of meta-heuristic algorithms
by: Zuriani, Mustaffa, et al.
Published: (2018) -
Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification
by: Mohamad Aseri, Nur Azieta, et al.
Published: (2022) -
Studying the Effect of Training Levenberg Marquardt Neural Network by Using Hybrid Meta-Heuristic Algorithms
by: Abubakar, A., et al.
Published: (2016)