Contents
Why is SVM margin 1?
since we can always multiply an arbitrary constant to both w and b without changing the hyperplane it represents. Therefore, a specific value of r (in this case r=1) doesn’t really impose any additional constraint on the candidate hyperplanes in this optimization problem.
What do you mean by hard margin SVM?
A hard margin means that an SVM is very rigid in classification and tries to work extremely well in the training set, causing overfitting.
Does soft margin SVM have a unique solution?
Yes, both the soft and hard formulations of standard SVM are convex optimization problems, hence have unique global optima.
Is Hard margin SVM unique?
The hard-margin SVM optimization problem has a unique solution only if the data are linearly separable, but it has no solution otherwise. This is because the constraints are impossible to satisfy if we can’t draw a hyperplane that separates the +1’s from the −1’s. To this end, we’ll talk about Soft-Margin SVMs.
Is SVM always convex?
So the SVM constraints are actually linear in the unknowns. Now any linear constraint defines a convex set and a set of simultaneous linear constraints defines the intersection of convex sets, so it is also a convex set.
What is the main difference between a SVM and SVR?
Boundary line: In SVM there are two lines other than Hyper Plane which creates a margin . The support vectors can be on the Boundary lines or outside it. This boundary line separates the two classes. In SVR the concept is same. Support vectors: This are the data points which are closest to the boundary.
What is soft margin?
The hard margin is a one which clearly separate positive and negative points. Soft margin is also called as noisy linear SVM which includes some miss-classified points. Solution to the soft margin is approximation of points which are miss-classified in linear decision boundary.
What is sparseness of SVM’s solution?
The SVM’s solution is a set of support vectors. And “sparseness of the solution” means that the “number of support vectors increases more slowly than linearly” when the problem size increases. As we know, Sparseness means thinly scattered or distributed (Meager).
What is a multi-class SVM method?
Multiclass SVMs are usually implemented by combining sev- eral two-class SVMs. The one-versus-all method using winner-takes-all strategy and the one-versus-one method implemented by max-wins vot- ing are popularly used for this purpose.