APPLICATION OF SUPERVISED LEARNING
SUPPORT VECTOR MACHINE SVM
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Detailed explanation-1: -Explanation: For a 4-class problem, you would have to train the SVM at least 4 times if you are using a one-vs-all method.
Detailed explanation-2: -In its most simple type SVM are applied on binary classification, dividing data points either in 1 or 0. For multiclass classification, the same principle is utilized. The multiclass problem is broken down to multiple binary classification cases, which is also called one-vs-one.
Detailed explanation-3: -In its most simple type, SVM doesn’t support multiclass classification natively. It supports binary classification and separating data points into two classes. For multiclass classification, the same principle is utilized after breaking down the multiclassification problem into multiple binary classification problems.
Detailed explanation-4: -The minimum number of support vectors is two for your scenario. You don’t need more than two here. All of the support vectors lie exactly on the margin. Regardless of the number of dimensions or size of data set, the number of support vectors could be as little as 2.