Online transfer learning and organic computing for deep space research and astronomy
- Published
- Accepted
- Subject Areas
- Adaptive and Self-Organizing Systems, Artificial Intelligence, Autonomous Systems, Data Mining and Machine Learning
- Keywords
- Online Transfer Learning, Transfer Learning, Organic Computing, Deep Space Research, Outer Space Explorations
- Copyright
- © 2019 Natarajan
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Online transfer learning and organic computing for deep space research and astronomy. PeerJ Preprints 7:e27581v1 https://doi.org/10.7287/peerj.preprints.27581v1
Abstract
Deep space exploration is the pillars within the field of outer space analysis and physical science. The amount of knowledge from numerous space vehicle and satellites orbiting the world of study are increasing day by day. This information collected from numerous experiences of the advanced space missions is huge. These information helps us to enhance current space knowledge and the experiences can be converted and transformed into segregated knowledge which helps us to explore and understand the realms of the deep space.. Online Transfer Learning (OTL) is a machine learning concept in which the knowledge gets transferred between the source domain and target domain in real time, in order to help train a classifier of the target domain. Online transfer learning can be an efficient method for transferring experiences and data gained from the space analysis data to a new learning task and can also routinely update the knowledge as the task evolves.
Author Comment
The paper talks about the online transfer learning as a mechanism to improve the current learning algorithms by transfer of knowledge in space technology and portray some concepts on top of existing mechanism used today by space agencies. This pre-print contains example futuristic concepts as proposed method and does not talk about implementation in detail.