Name
Abstract | ||
|---|---|---|
By putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main applications of interest, and thus the constraints and requirements on solutions, which include comput... |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/MSP.2011.942319 | IEEE Signal Processing Magazine |
Keywords | Field | DocType |
Machine learning,Learning systems,Speech recognition,Emotion recognition,Distributed databases | Robot learning,Computer vision,Multi-task learning,Instance-based learning,Active learning (machine learning),Inductive transfer,Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 6 | 1053-5888 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tülay Adali | 1 | 1690 | 126.40 |
| David J. Miller | 2 | 421 | 60.29 |
| Konstantinos I. Diamantaras | 3 | 299 | 36.06 |
| Jan Larsen | 4 | 55 | 6.62 |