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COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK

Long Short-Term Memory (LSTM) models are a type of recurrent neural network (RNN) well-suited for tasks requiring the model to remember long-term dependencies. This makes them a promising approach for ET rate estimation, as ET is a process that is influenced by various factors that may occur over lo...

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Main Author: HILMI, MUHAMMAD ZAHID
Format: Thesis
Language:English
Published: 2023
Subjects:
T Technology (General)
Online Access:http://utpedia.utp.edu.my/id/eprint/24854/1/2023_PhD%20in%20IT_thesis%20submission_1900298_Muhammad%20Zahid%20bin%20Hilmi.pdf
http://utpedia.utp.edu.my/id/eprint/24854/
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spelling oai:utpedia.utp.edu.my:248542023-09-14T07:11:39Z http://utpedia.utp.edu.my/id/eprint/24854/ COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK HILMI, MUHAMMAD ZAHID T Technology (General) Long Short-Term Memory (LSTM) models are a type of recurrent neural network (RNN) well-suited for tasks requiring the model to remember long-term dependencies. This makes them a promising approach for ET rate estimation, as ET is a process that is influenced by various factors that may occur over long periods. 2023-08 Thesis NonPeerReviewed text en http://utpedia.utp.edu.my/id/eprint/24854/1/2023_PhD%20in%20IT_thesis%20submission_1900298_Muhammad%20Zahid%20bin%20Hilmi.pdf HILMI, MUHAMMAD ZAHID (2023) COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK. Masters thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
HILMI, MUHAMMAD ZAHID
COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
description Long Short-Term Memory (LSTM) models are a type of recurrent neural network (RNN) well-suited for tasks requiring the model to remember long-term dependencies. This makes them a promising approach for ET rate estimation, as ET is a process that is influenced by various factors that may occur over long periods.
format Thesis
author HILMI, MUHAMMAD ZAHID
author_facet HILMI, MUHAMMAD ZAHID
author_sort HILMI, MUHAMMAD ZAHID
title COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
title_short COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
title_full COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
title_fullStr COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
title_full_unstemmed COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
title_sort comparative study of surrogate techniques for hyperparameter optimization in recurrent neural network
publishDate 2023
url http://utpedia.utp.edu.my/id/eprint/24854/1/2023_PhD%20in%20IT_thesis%20submission_1900298_Muhammad%20Zahid%20bin%20Hilmi.pdf
http://utpedia.utp.edu.my/id/eprint/24854/
_version_ 1778164441052872704
score 13.222552

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