Im studying about support vector machines; on the dual formulation of SVM and I couldnt understand why the objective function is concave wrt $\alpha$. I know I can use the definition on concavity but I was hoping someone could give me an intuitive explanation on why it would be concave.
An example of an explanation for w would be: the objective function in convex in w because you can visualize points from 2 classes that scattered in a 2D plane and are linearly separable. From there, it is easy to see that there is only one configuration of the separating line that gives the lowest error. If you rotate this line around you will get larger errors.