Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data-Reference-Cited by-同舟云学术

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

Published:2017-12-13 Issue:12 Volume:12 Page:e0188746
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Ye Fei^ORCID

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference63 articles.

1. Human action recognition with graph-based multiple-instance learning;Y. Yi;Pattern Recognition,2016

2. A novel hierarchical framework for human action recognition;H. Chen,2016

3. Multi-view human action recognition using 2d motion templates based on mhis and their hog description;F. Murtaza,2017

4. Human action recognition using histogram of motion intensity and direction from multiple views

5. The spatial laplacian and temporal energy pyramid representation for human action recognition using depth sequences;X. Ji,2017

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