A video prediction method by using long short-term memory-based adaptive structural learning of deep belief network and its investigation of input sequence length for data structure Online publication date: Wed, 02-Jun-2021
by Shin Kamada; Takumi Ichimura
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 10, No. 2/3, 2021
Abstract: In our research, the adaptive structural learning method using RBM and DBN has been developed as a deep learning model, which can find a suitable size of network structure for given input space during its training. The adaptive RBM and DBN were extended to the time-series prediction using the idea of long short-term memory (LSTM). Our previous research tackled the problems for supervised learning, in this paper, we challenge to reveal the power of our proposed method in the video recognition using Moving MNIST, which is a benchmark dataset for video recognition. Adaptive LSTM-DBN trained the time-series movement from the video data and it showed higher future prediction performance than the existing LSTM models (more than 90% for test data). Moreover, the detailed prediction results were investigated by training adaptive LSTM-DBN with various length of input sequence in the experiment results.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Intelligence Studies (IJCISTUDIES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook