Name
Abstract | ||
|---|---|---|
•We propose a risk-based safe Laplacian Regularized Least Squares method.•Risk degree is computed by analyzing different characteristics in RLS and LapRLS.•The performance of proposed algorithm is never significantly inferior to that of RLS and LapRLS.•The performance of our algorithm is relatively stable with respect to the tradeoff parameter λ. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.eswa.2015.09.017 | Expert Systems with Applications |
Keywords | Field | DocType |
Semi-supervised learning,Laplacian Regularized Least Squares,Safe mechanism,Risk degree | Data mining,Semi-supervised learning,Pattern recognition,Regularized least squares,Computer science,Supervised learning,Artificial intelligence,Machine learning,Laplace operator | Journal |
Volume | Issue | ISSN |
45 | C | 0957-4174 |
Citations | PageRank | References |
3 | 0.39 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Gan | 1 | 6 | 1.45 |
| Zhizeng Luo | 2 | 49 | 11.65 |
| Yao Sun | 3 | 5 | 0.75 |
| Xugang Xi | 4 | 27 | 6.02 |
| Nong Sang | 5 | 475 | 72.22 |
| Rui Huang | 6 | 1179 | 83.33 |