Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of multi-label classification. We consider linear classifiers and propose to learn a prior over the space of labels to directly leverage the performance of such methods. This prior takes the form of a quadratic function of the labels and permits to encode both attractive and repulsive relations between labels. We cast this problem as a structured prediction one aiming at optimizing either the accuracies of the predictors or the F 1-score. This leads to an optimization problem closely related to the max-cut problem, which naturally leads to semidefinite and spectral relaxations. We show on standard datasets how such a general prior can improve the performances of multi-label techniques. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | ENCODE,Mathematical optimization,Structured prediction,Multi-label classification,Quadratic function,Artificial intelligence,Optimization problem,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1506.01829 | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rémi Lajugie | 1 | 101 | 4.68 |
| Piotr Bojanowski | 2 | 848 | 28.36 |
| Sylvain Arlot | 3 | 65 | 6.87 |
| Francis Bach | 4 | 11490 | 622.29 |