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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What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
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1
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2
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1 and 2
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None of these
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Explanation:
Detailed explanation-1: -The kernel in the support vector machine is responsible for transforming the input data into the required format. Some of the kernels used in support vector machines are linear, polynomial, and radial basis functions (RBF). To create a non-linear hyperplane, we use RBF and the Polynomial function.
Detailed explanation-2: -Kernels are measures of similarity, i.e. s(a, b) > s(a, c) if objects a and b are considered “more similar” than objects a and c . A kernel must also be positive semi-definite. There are a number of ways to convert between a distance metric and a similarity measure, such as a kernel.
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