For questions about the hinge loss, which is typically used for "maximum-margin" classification, most notably for support vector machines (SVMs).
Questions tagged [hinge-loss]
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How should we interpret this figure that relates the perceptron criterion and the hinge loss?
I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following:
The perceptron criterion is a shifted version of the hinge-loss used in…
The Pointer
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What is the definition of the hinge loss function?
I came across the hinge loss function for training a neural network model, but I did not know the analytical form for the same.
I can write the mean squared error loss function (which is more often used for regression) as
$$\sum\limits_{i=1}^{N}(y_i…
hanugm
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Why is the Hinge Loss defined this way?
I have a question regarding the Hinge Loss function used for classifiers and in general the "max-margin" types of classifiers, it is defined as $$max(0,1-t*y)$$ where $t$ is the intended output, either $-1$ or $1$ and $y$ is the classifier output.…
Riccardo Caiulo
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What is the definition of the "cost" function in the SVM's objective function?
In a course that I am attending, the cost function of a support vector machine is given by
$$J(\theta)=\sum_{i=1}^{m} y^{(i)} \operatorname{cost}_{1}\left(\theta^{T} x^{(i)}\right)+\left(1-y^{(i)}\right) \operatorname{cost}_{0}\left(\theta^{T}…
jr123456jr987654321
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