Name
Abstract | ||
|---|---|---|
This paper deals with active sampling of graph nodes representing training data for binary classification. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that labels across neighboring nodes are correlated according to a categorical Markov random field (MRF). This model is further relaxed to a Gaus... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2018.2866812 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Predictive models,Minimization,Laplace equations,Numerical models,Training,Correlation,Covariance matrices | Heuristic,Binary classification,Categorical variable,Markov random field,Algorithm,Artificial intelligence,Sampling (statistics),Rendering (computer graphics),Classifier (linguistics),Machine learning,Mathematics,Scalability | Journal |
Volume | Issue | ISSN |
66 | 19 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitris Berberidis | 1 | 45 | 7.47 |
| Georgios B. Giannakis | 2 | 4977 | 340.58 |