Name
Abstract | ||
|---|---|---|
This paper presents a sequential learning framework for sensors in a network, where a few sensors assume the role of an instructor to train other sensors in the network. The instructors provide estimated labels for measurements of new sensors. These labels are possibly noisy, because a classifier of the instructor may not be perfect. A recursive density estimator is proposed to obtain the true mea... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2018.2791898 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Noise measurement,Density measurement,Data models,Training data,Estimation,Learning systems,Computational modeling | Data modeling,Noise measurement,Computer science,Artificial intelligence,Classifier (linguistics),Sequence learning,Wireless sensor network,Recursion,Machine learning,Estimator,Kernel density estimation | Journal |
Volume | Issue | ISSN |
29 | 10 | 2162-237X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nurali Virani | 1 | 16 | 4.26 |
| Shashi Phoha | 2 | 201 | 39.47 |
| Ray, A. | 3 | 832 | 184.32 |