Nonlinear optimization and support vector machines
By Veronica Piccialli, Marco Sciandrone. 2018
48 citations recorded by the source corpus.
Support Vector Machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
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Citations in the corpus
- Global optimization issues in deep network regression: an overview (2018, methodology): “We refer to [41] for a survey on semi-supervised methods which are not covered in this paper, to [36,53] and references therein for complete surveys on mathematical optimization for SVM, and to [107] for a survey on solution approaches for the convex training problem of different SVMs.”