Name
Abstract | ||
|---|---|---|
We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large-scale datasets. Empirical evidences illustrate the potential of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1961189.1961200 | ACM TIST |
Keywords | Field | DocType |
nonconvex problem,false negatives rate,bipartite ranking problem,batch algorithm,online learning,np classification,nonconvex svm,large-scale datasets,neyman-pearson,nonconvex neyman-pearson classification,dc algorithm,dc programming,classification problem,particular importance,empirical evidence | Online learning,Ranking,Computer science,Bipartite graph,Stochastic gradient method,Algorithm,Artificial intelligence,Dc programming,Machine learning | Journal |
Volume | Issue | ISSN |
2 | 3 | 2157-6904 |
Citations | PageRank | References |
8 | 0.70 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilles Gasso | 1 | 207 | 16.52 |
| Aristidis Pappaioannou | 2 | 8 | 0.70 |
| Marina Spivak | 3 | 46 | 3.50 |
| Léon Bottou | 4 | 11754 | 1364.56 |