Deterministic convergence analysis for regularized long short-term memory and its application to regression and multi-classification problems

Long short-term memory (LSTM) is a recurrent neural network (RNN) framework designed to solve the gradient disappearance and explosion problems of traditional RNNs. In recent years, LSTM has become a state-of-the-art model for solving various machine-learning problems. This paper propose a novel reg...

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Vydáno v:	Engineering applications of artificial intelligence Ročník 133; s. 108444
Hlavní autoři:	Kang, Qian, Yu, Dengxiu, Cheong, Kang Hao, Wang, Zhen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.07.2024
Témata:	Batch gradient algorithm Convergence Long short-term memory Regularization Long short-term memory Batch gradient algorithm Regularization Convergence
ISSN:	0952-1976, 1873-6769
On-line přístup:	Získat plný text
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