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In SVM problems, we cannot directly apply gradient descent but we can apply Subgradient descent.

In SVM problems, we cannot directly apply gradient descent but we can apply Subgradient descent. Correct Answer True

In SVM problems we cannot directly apply gradient descent but we can apply Subgradient descent. Because SVM objective is not continuously differentiable and we cannot apply gradient descent. And Sub-gradient descent can be used to solve this non-differentiable SVM objective function.

Related Questions

Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting. Which of the following is best option would you more likely to consider iterating SVM next time?
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