Name
Abstract | ||
|---|---|---|
A sparse deep learning method is proposed to overcome overfitting risk of deep networks with a large number of nodes and layers. Deep stacking network (DSN) is a classic and effective deep learning method, and its sparse form is presented to generate the sparse deep learning method. In DSN, output labels are encoded as a series consisted of 1 and 0. This coding strategy makes output labels to be s... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2819674 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Fault diagnosis,Deep learning,Electric motors,Mechanical engineering,Feature extraction,Neural nets | Kernel (linear algebra),Pattern recognition,Computer science,Coding (social sciences),Feature extraction,Real-time computing,Regularization (mathematics),Artificial intelligence,Overfitting,Deep learning,NASA Deep Space Network,Binary number | Journal |
Volume | Issue | ISSN |
14 | 7 | 1551-3203 |
Citations | PageRank | References |
4 | 0.53 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuang Sun | 1 | 70 | 8.35 |
| Meng Ma | 2 | 82 | 12.29 |
| Zhao Zhibin | 3 | 49 | 15.04 |
| XueFeng Chen | 4 | 441 | 55.44 |