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Support Vector Machine
arXiv (Cornell University)
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
2023
Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in sub…
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Support Vector Machine

Set of methods for supervised statistical learning

In machine learning, support vector machines (SVMs , also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

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arXiv (Cornell University)
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
2023
Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in substantial degradation in the convergence rate of training. To accelerate the learning procedure for diverse participants in a multi-task federated setting, more efficient and robust methods need to be developed. In this paper, we design an efficient iterative d…
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