Name
Title | ||
|---|---|---|
Learn-on-the-go: Autonomous cross-subject context learning for internet-of-things applications. |
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.
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICCAD.2017.8203800 | ICCAD |
Keywords | Field | DocType |
Autonomous transfer learning,cross-subject boosting,activity recognition | Training set,Activity recognition,Computer science,Internet of Things,Exploit,Real-time computing,Artificial intelligence,Graph model,Machine learning,Performance improvement,Scalability | Conference |
ISSN | ISBN | Citations |
1933-7760 | 978-1-4503-5950-4 | 0 |
PageRank | References | Authors |
0.34 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramin Fallahzadeh | 1 | 40 | 6.63 |
| Parastoo Alinia | 2 | 139 | 5.49 |
| Hassan Ghasemzadeh | 3 | 656 | 61.36 |