Contents
What does the margin look like in SVM?
Basically the margin is a no man’s land. There will never be any data point inside the margin. (Note: this can cause some problems when data is noisy, and this is why soft margin classifier will be introduced later) For another hyperplane, the margin will look like this : As you can see, Margin B is smaller than Margin A.
What is the idea of soft margin formulation?
Soft Margin Formulation. This idea is based on a simple premise: allow SVM to make a certain number of mistakes and keep margin as wide as possible so that other points can still be classified correctly. This can be done simply by modifying the objective of SVM.
Which is the best answer to the SVM question?
SVM Answer: The one that maximizes the distance to the closest data points from both classes. We say it is the hyperplane with maximum margin.
When to set the margin to 1 or 0?
When solving for a hyperplane specified by , 0 one can always set the margin to 1; True (as long as it is separable) Lyle H Ungar, University of Pennsylvania 9
Which is the decision function of a SVMs?
•SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. •The decision function is fully specified by a (usually very small) subset of training samples, the support vectors. •This becomes a Quadratic programming problem that is easy to solve by standard methods.
How are support vectors used in a SVM?
Support Vector Machine (SVM) Support vectors Maximize margin. •SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. •The decision function is fully specified by a (usually very small) subset of training samples, the support vectors.
How to answer the SVM questions about hyperplanes?
SVM Questions Lyle Ungar Lyle H Ungar, University of Pennsylvania 2 Hyperplanes Given the hyperplane defined by the line “ y = x 1- 2x 2 “ y = (1,-2)Tx = wTx Is this point correctly predicted? 1) y = 1, x= (1,0) ?