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- W87630839 abstract "We start discussing supervised learning algorithms with a simple example, the K -nearest neighbors. The main focus is, however, on support vector machines. We approach the topic from defining optimal margins on linearly separable data sets, and then we introduce soft margins to allow inseparable classes. Support vector machines capture nonlinear structures by using the kernel trick: the distance of data points in a high-dimensional embedding space is calculated without specifying the embedding function itself. Sparsity is central to the learned model, but we can trade off this sparsity for a formulation by linear equations, which has an efficient quantum variant. The generalization performance of support vector machines is well understood, especially with the typically used loss function, the hinge loss. This makes the algorithms a form of convex optimization, albeit alternative formulations exist. The actual computational complexity of these algorithms is still an open problem." @default.
- W87630839 created "2016-06-24" @default.
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- W87630839 date "2014-01-01" @default.
- W87630839 modified "2023-09-25" @default.
- W87630839 title "Supervised Learning and Support Vector Machines" @default.
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- W87630839 doi "https://doi.org/10.1016/b978-0-12-800953-6.00007-4" @default.
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