CS 451 Quiz 15
SVMs, Kernels
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The maximum value of the Gaussian kernel is 1
Which Gaussian kernel has a narrower ("sharper") peak?
It is important to perform feature normalization before using the Gaussian kernel
For SVMs employing kernels, how are the "landmarks" chosen?
A SVM with a linear kernel is the same as an SVM with no kernel
An SVM with a linear kernel is very similar to
What is a good "regime" for SVMs with Gaussian kernels (where N = number of features and M = number of training examples)?
Gaussian kernels measure the ________ between feature x and landmark l(i)
If you want to use the one-vs-all approach with SVMs for multi-class classification with K classes, how many different SVMs do you need to train?
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