A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification
| Autor: | Ricardo Ñanculef, Johan A. K. Suykens, Emanuele Frandi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
FOS: Computer and information sciences
050210 logistics & transportation 021103 operations research Computer science 05 social sciences 0211 other engineering and technologies Tangent Machine Learning (stat.ML) Scale (descriptive set theory) 02 engineering and technology Field (computer science) Machine Learning (cs.LG) Support vector machine Computer Science - Learning Range (mathematics) Frank–Wolfe algorithm Statistics - Machine Learning Optimization and Control (math.OC) 0502 economics and business FOS: Mathematics Benchmark (computing) Mathematics - Optimization and Control Algorithm |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2015.7280402 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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