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Efficient Online Learning Algorithms Based on LSTM Neural Networks
Ergen, Tolga ; Kozat, Suleyman Serdar
IEEE transaction on neural networks and learning systems, 2018-08, Vol.29 (8), p.3772-3783
United States: IEEE
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Título:
Efficient Online Learning Algorithms Based on LSTM Neural Networks
Autor:
Ergen, Tolga
;
Kozat, Suleyman Serdar
Assuntos:
Algorithms
;
Architecture
;
Complexity theory
;
Computational modeling
;
Computer applications
;
Computer architecture
;
Control methods
;
Data models
;
Distance learning
;
Error detection
;
Extended Kalman filter
;
Filtration
;
Gated recurrent unit (GRU)
;
Internet
;
Kalman filtering
;
Learning algorithms
;
long short term memory (LSTM)
;
Machine learning
;
Neural networks
;
Online instruction
;
online learning
;
Parameter estimation
;
particle filtering (PF)
;
Recurrent neural networks
;
regression
;
Short term memory
;
stochastic gradient descent (SGD)
;
Stochasticity
;
Training
É parte de:
IEEE transaction on neural networks and learning systems, 2018-08, Vol.29 (8), p.3772-3783
Notas:
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição:
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.
Editor:
United States: IEEE
Idioma:
Inglês
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