Learn-on-the-go: Autonomous cross-subject context learning for internet-of-things applications

Ramin Fallahzadeh, Parastoo Alinia, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Developing machine learning algorithms for applications of Internet-of-Things requires collecting a large amount of labeled training data, which is an expensive and labor-intensive process. Upon a minor change in the context, for example utilization by a new user, the model will need re-training to maintain the initial performance. To address this problem, we propose a graph model and an unsupervised label transfer algorithm (learn-on-the-go) which exploits the relations between source and target user data to develop a highly-accurate and scalable machine learning model. Our analysis on real-world data demonstrates 54% and 22% performance improvement against baseline and state-of-the-art solutions, respectively.

Original languageEnglish (US)
Title of host publication2017 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages360-367
Number of pages8
ISBN (Electronic)9781538630938
DOIs
StatePublished - Dec 13 2017
Externally publishedYes
Event36th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017 - Irvine, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2017-November
ISSN (Print)1092-3152

Other

Other36th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017
Country/TerritoryUnited States
CityIrvine
Period11/13/1711/16/17

Keywords

  • Activity recognition
  • Autonomous transfer learning
  • Cross-subject boosting

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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