What does Alpha mean in SVM?
Lagrangian multiplier
Lagrangian multiplier, usually denoted by α is a vector of the weights of all the training points as support vectors. Suppose there are m training examples. Then α is a vector of size m.
What is Alpha in dual form of SVM?
Important observations from dual form svm are: For every Xi we have alpha(i) Alpha(i) is greater than zero only for support vectors and for all other points it is 0. So while prediction for a query point only support vectors do matter.
What is Lagrangian in SVM?
The idea used in Lagrange multiplier is that the gradient of the objective function f, lines up either in parallel or anti-parallel direction to the gradient of the constraint g, at an optimal point. In such case, one the gradients should be some multiple of another.
How is the Lagrange functional of the SVM formulated?
Under such a formulation the problem is convex. One can show that margin maximization reduces the VC dimension. The Lagrange functional for the primal problem for is: where and are Lagrange multipliers. The primal problem is formulated as: Those points for which the equation holds are called support vectors.
What does the Alpha mean in the dual form of the SVM?
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Which is a support vector in the SVM?
Those points for which the equation holds are called support vectors. After training the support vector machine and deriving Lagrange multipliers (they are equal to 0 for non-support vectors) one can classify a company described by the vector of parameters using the classification rule:
What does the C mean in the SVM?
A lower C means higher regularization. A lower C thus prevents overfitting. Having a lower C means the optimization doesn’t care about the loss too much, and it will prefer to have a simpler model (lower ||w|| ) rather than go out of its way to reduce the loss. What does this mean in the dual?