Real-time action recognition based on a modified Deep Belief Network model

2014 ◽  
Author(s):  
Haiting Zhang ◽  
Fengyu Zhou ◽  
Wei Zhang ◽  
Xianfeng Yuan ◽  
Zhuming Chen
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Network Model ◽  
Deep Belief Network ◽  
Belief Network

An ontology-based deep belief network model

Computing ◽  
2021 ◽  
Author(s):  
Xiulei Liu ◽  
Ruoyu Chen ◽  
Qiang Tong ◽  
Zhihui Qin ◽  
Qinfu Shi ◽  
...  
Keyword(s):  
Network Model ◽  
Deep Belief Network ◽  
Belief Network

scholarly journals Real-time classification and sensor fusion with a spiking deep belief network

2013 ◽  
Vol 7 ◽  
Author(s):  
Peter O'Connor ◽  
Daniel Neil ◽  
Shih-Chii Liu ◽  
Tobi Delbruck ◽  
Michael Pfeiffer
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Deep Belief Network ◽  
Belief Network ◽  
Real Time Classification

scholarly journals A Cycle Deep Belief Network Model for Multivariate Time Series Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shuqin Wang ◽  
Gang Hua ◽  
Guosheng Hao ◽  
Chunli Xie
Keyword(s):  
Time Series ◽  
Network Model ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Deep Belief Network ◽  
Learning Ability ◽  
Series Data ◽  
Temporal Data ◽  
Belief Network ◽  
Data Objects

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

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