TY - GEN
T1 - Learn-on-the-go
T2 - 36th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017
AU - Fallahzadeh, Ramin
AU - Alinia, Parastoo
AU - Ghasemzadeh, Hassan
N1 - Funding Information:
This work was supported in part by the National Science Foundation under grant CNS-1566359 and the National Institutes of Health under Grant No.: 1R21NR015410-01A1. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - 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.
AB - 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.
KW - Activity recognition
KW - Autonomous transfer learning
KW - Cross-subject boosting
UR - http://www.scopus.com/inward/record.url?scp=85043503842&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043503842&partnerID=8YFLogxK
U2 - 10.1109/ICCAD.2017.8203800
DO - 10.1109/ICCAD.2017.8203800
M3 - Conference contribution
AN - SCOPUS:85043503842
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 360
EP - 367
BT - 2017 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 November 2017 through 16 November 2017
ER -