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Following the notion of standard hard margin SVM, it is believed that data from two classes can be separated by a hard margin.
In developing our SVM VS tool, a hard margin c=100,000 was used.
Fig. 11 An example of hard margin MKL with primal formulation.
In fact, it has been pointed out in [39] that the hard margin MKL is a method which only selects the base kernels with minimum objective.
This formulation is called hard margin SVM and implies that the problem is well posed, separable and that a solution exists.
Thus, in a case where the single features are already discriminant, the hard margin MKL will be unable to exhibit any better performance than the single features.
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The hard-margin support vector machines are decision functions giving the maximum margin.
Hard-margin SVMs may not always exist in real-life problems.
According to Joachims [ 30] "A small value for C will increase the number of training errors, while a large C will lead to a behavior similar to that of a hard-margin SVM", therefore not allowing classification errors and having the tendency to over-fit.
(4) and (5) into Eq. (3), the QP problem becomes the maximization of the following expression: (6) L = ∑ i = 1 n α i − 1 2 ∑ i = 1 n ∑ j = 1 n α i α j y i y j (x i · x j ) under the constraints (7) ∑ i = 1 n α i y i = 0, 0 ≤ α i ≤ C, i = 1, 2, …, n where C is a penalty for training errors for soft-margin SVM and is equal to infinity for hard-margin SVM.
Sport's hard, the margin between winning and losing is tiny.
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