# Questions tagged [hinge-loss]

The hinge loss function is defined as $l(y, t) = max(0, 1 - t \cdot y)$

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### SVM behavior when regularization parameter equals 0

I read on this Wikipedia page the following about soft-margin SVM: "The parameter $λ$ determines the trade-off between increasing the margin size and ensuring that the $x_i$ lie on the correct ...
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### Hinge Loss understanding and proof

I hope this doesn't come off as a silly question, but I am looking at SVMs and in principle I understand how they work. The idea is to maximize the margin between different classes of point (within ...
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### How is hinge loss related to primal form / dual form of SVM

I'm learning SVM and many classic tutorials talk about the formulation of SVM problem as a convex optimization problem: i.e. We have the objective function with slack variables and subject to ...
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For knwoledge graph completion, it is very common to use margin-based ranking loss In the paper:margin-based ranking loss is defined as  \min \sum_{(h,l,t)\in S} \sum_{(h',l,t')\in S'}[\gamma + d(...