Questions tagged [hinge-loss]
The hinge loss function is defined as $l(y, t) = max(0, 1 - t \cdot y)$
9
questions
0
votes
0
answers
9
views
Correlation between hinge loss function, Langrage function and ai
The function $f(w,b) = \frac{1}{2} ||w||^2$ is our objective function while our constraints are all the correct classifications of the data points expressed as $g(w,b) = \sum_{i=1}^{l} (y_i (x_i \cdot ...
- 1
2
votes
1
answer
2k
views
how to implement squared hinge loss in pytorch
does anyone have any advice on how to implement this loss in order to use it with a convolutional neural network?
Also, how should I encode the labels of my training data? We were using one hot ...
- 31
1
vote
0
answers
1k
views
0
votes
1
answer
57
views
Hinge loss question
Hinge loss is usually defined as
$$L(y,\hat{y}) = max(0,1-y\hat{y}) $$
What I don't understand is why are we comparing zero with $1-y\hat{y}$ instead of some other constant. Why not make it $2-y\hat{...
- 13
3
votes
2
answers
510
views
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 ...
- 51
3
votes
1
answer
630
views
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 ...
- 179
2
votes
1
answer
1k
views
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 ...
- 145
1
vote
1
answer
571
views
Why margin-based ranking loss is reversed in these two papers?
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(...
- 319
12
votes
1
answer
4k
views
What's the relationship between an SVM and hinge loss?
My colleague and I are trying to wrap our heads around the difference between logistic regression and an SVM. Clearly they are optimizing different objective functions. Is an SVM as simple as saying ...
- 1,026