How do kernels separate non-linear data?1 pointBy projecting data into low-dimensional spaceBy projecting data into high-dimensional spaceBy generating new non-linear data pointsNone of the above
Question
How do kernels separate non-linear data?1 pointBy projecting data into low-dimensional spaceBy projecting data into high-dimensional spaceBy generating new non-linear data pointsNone of the above
Solution
Kernels separate non-linear data by projecting data into high-dimensional space. Here's a step-by-step explanation:
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The kernel function takes data as input and transforms it into the required form.
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The kernel function uses the inner product of two input vectors and transforms it into the inner product in a high or infinite dimensional feature space. This is known as the "kernel trick".
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By doing this, the kernel function is able to convert a non-linear separable problem to a linear separable problem by adding more dimension to it.
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Once the data is transformed and projected into this high-dimensional space, it can be separated linearly.
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This is particularly useful in support vector machines (SVMs), where the kernel trick is used to create the maximum margin hyperplane in the transformed space
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Which of the following is a non linear data structure ?1 pointListStringStacksNone of the above
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