Motivation Factors Analysis and Policy Research of Deep Learning

DOI

10.2991/mmebc-16.2016.255How to use a DOI?

Keywords

Deep learning, ISM, AHP, importance degree, motivation factors, policy research.

Abstract

This article uses Interpretative Structural Modeling Method(ISM) to build deep learning promotion structure diagram of the influence factors, then uses Analytic Hierarchy Process(AHP) to determine the relative importance of various factors, according to the evaluation results it is concluded that the policy guidelines for deep learning to promote the influence degree of the relative maximum conclusion, put forward to promote deep learning better and faster, the government related department should publish relevant policy recommendations, embodied in more research funds, set up special introduction and training of research institutions and researchers, to perfect the theory system. At the same time, the improvement of social awareness will attract more high-tech companies in product research and development, making deep learning applied in more fields.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liyao Bu
PY  - 2016/06
DA  - 2016/06
TI  - Motivation Factors Analysis and Policy Research of Deep Learning
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1252
EP  - 1256
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.255
DO  - 10.2991/mmebc-16.2016.255
ID  - Bu2016/06
ER  -