CODEWITHSUNDEEP
machine-learningsvm
jarjar
jarjar

Reputation: 81

In SVM, can a support vector not be a training sample?

For all SVM versions, like c-svm, v-svm, soft margin svm etc., can a support vector not be a training sample?

Upvotes: 2

Views: 333

Answers (1)

Fred Foo
Fred Foo

Reputation: 363787

No, it can't. A support vector is always a sample from the training set.

This is a good thing, because it means SVMs are oblivious to the internal structure of their samples and their support vectors. Only the kernel function, which is separate from the SVM proper, has to know about the structure of samples. While most kernels operate on vectors of numbers, there exist kernels that operate on strings, trees, graphs, you name it.

(Note that linear support vector machines can be trained without taking support vectors into account. I.e., when you train a linear model under hinge loss with appropriate regularization using an algorithm such as SGD, you get a model that is equivalent to an SVM with a linear kernel, but where the support vectors are implicit.)

Upvotes: 1

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