Multi-View Temporal Ensemble for Classification of Non-Stationary Signals
In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the...
Main Authors: | B. H. D. Koh, Wai Lok Woo |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8662555/ |
Similar Items
-
Multi-Channel Fusion Classification Method Based on Time-Series Data
by: Xue-Bo Jin, et al.
Published: (2021-06-01) -
Deep Temporal Convolution Network for Time Series Classification
by: Bee Hock David Koh, et al.
Published: (2021-01-01) -
Time-Series Classification based on Fusion Features of Sequence and Visualization
by: Baoquan Wang, et al.
Published: (2020-06-01) -
An Effective Confidence-Based Early Classification of Time Series
by: Junwei Lv, et al.
Published: (2019-01-01) -
A Multirepresentational Fusion of Time Series for Pixelwise Classification
by: Danielle Dias, et al.
Published: (2020-01-01)