Name
Abstract | ||
|---|---|---|
Frank-Wolfe algorithms have recently regained the attention of the Machine Learning community. Their solid theoretical properties and sparsity guarantees make them a suitable choice for a wide range of problems in this field. In addition, several variants of the basic procedure exist that improve its theoretical properties and practical performance. In this paper, we investigate the application of some of these techniques to Machine Learning, focusing in particular on a Parallel Tangent (PARTAN) variant of the FW algorithm for SVM classification, which has not been previously suggested or studied for this type of problem. We provide experiments both in a standard setting and using a stochastic speed-up technique, showing that the considered algorithms obtain promising results on several medium and large-scale benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IJCNN.2015.7280402 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Field | DocType | Volume |
Online machine learning,Mathematical optimization,Computer science,Support vector machine,Frank–Wolfe algorithm,Tangent,Artificial intelligence,Machine learning | Journal | abs/1502.01563 |
ISSN | Citations | PageRank |
2161-4393 | 1 | 0.35 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emanuele Frandi | 1 | 30 | 3.88 |
| Ricardo Ñanculef | 2 | 1 | 0.35 |
| Johan A. K. Suykens | 3 | 635 | 53.51 |