Gradient Flows for L2 Support Vector Machine Training
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Abstract (arXiv)
We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations. We thus assume a continuous time perspective on a machine learning problem which may be of interest for implementations on (re)emerging hardware platforms such as analog- or quantum computers.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 8 Aug 2022.
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7 of 29 references.
References · 7
- 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
- 2019On Exact Computation with an Infinitely Wide Neural Netcited by 573
- 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 169
- 2017A Survey of Neuromorphic Computing and Neural Networks in Hardwarecited by 85
- 2021On the Equivalence between Neural Network and Support Vector Machinecited by 17
- 2022Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learningcited by 5
- 2017Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifierscited by 1
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