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Article

Transfer Learning in Smart Environments

1
Lero—The Irish Software Research Centre, National University of Ireland, H91 CF50 Galway, Ireland
2
Insight Centre for Data Analytics, National University of Ireland, H91 CF50 Galway, Ireland
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2020), Dublin, Ireland, 25–28 August 2020.
Academic Editor: Isaac Triguero
Mach. Learn. Knowl. Extr. 2021, 3(2), 318-332; https://0-doi-org.brum.beds.ac.uk/10.3390/make3020016
Received: 31 December 2020 / Revised: 1 March 2021 / Accepted: 19 March 2021 / Published: 29 March 2021
(This article belongs to the Special Issue Selected Papers from CD-MAKE 2020 and ARES 2020)
The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labeling, network training, and fine-tuning of models. Sharing and reuse of these elaborated resources between intelligent systems of different environments, which is known as transfer learning, would facilitate the adoption of cognitive services for the users and accelerate the uptake of intelligent systems in smart building and smart city applications. Currently, machine learning processes are commonly built for intra-organization purposes and tailored towards specific use cases with the assumption of integrated model repositories and feature pools. Transferring such services and models beyond organization boundaries is a challenging task that requires human intervention to find the matching models and evaluate them. This paper investigates the potential of communication and transfer learning between smart environments in order to empower a decentralized and peer-to-peer ecosystem for seamless and automatic transfer of services and machine learning models. To this end, we explore different knowledge types in the context of smart built environments and propose a collaboration framework based on knowledge graph principles for describing the machine learning models and their corresponding dependencies. View Full-Text
Keywords: knowledge graph; transfer learning; internet of things; cognitive models knowledge graph; transfer learning; internet of things; cognitive models
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MDPI and ACS Style

Anjomshoaa, A.; Curry, E. Transfer Learning in Smart Environments. Mach. Learn. Knowl. Extr. 2021, 3, 318-332. https://0-doi-org.brum.beds.ac.uk/10.3390/make3020016

AMA Style

Anjomshoaa A, Curry E. Transfer Learning in Smart Environments. Machine Learning and Knowledge Extraction. 2021; 3(2):318-332. https://0-doi-org.brum.beds.ac.uk/10.3390/make3020016

Chicago/Turabian Style

Anjomshoaa, Amin, and Edward Curry. 2021. "Transfer Learning in Smart Environments" Machine Learning and Knowledge Extraction 3, no. 2: 318-332. https://0-doi-org.brum.beds.ac.uk/10.3390/make3020016

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