CS 451 Quiz 15
SVMs, Kernels
* Required
Email address
*
Your email
Support vector machines are also know as
*
1 point
large margin classifiers
small margin classifiers
In the context of SVMs, "margin" refers to
*
1 point
the margin of error when classifying training examples
the marginal distribution of the misclassified training examples
the distance from the decision boundary to the nearest training examples
If we have data that is not linearly separable, then SVMs
*
1 point
cannot be employed successfully
can still be employed successfully by using a smaller value C
can still be employed successfully by using a very large value C
The maximum value of the Gaussian kernel is 1
*
1 point
True
False
Which Gaussian kernel has a narrower ("sharper") peak?
*
1 point
sigma^2 = 0.5
sigma^2 = 5
It is important to perform feature normalization before using the Gaussian kernel
*
1 point
True
False
Gaussian kernels measure the ________ between feature x and landmark l(i)
*
1 point
distance / dissimilarity
proximity / similarity
For SVMs employing kernels, how are the "landmarks" chosen?
*
1 point
Some of the training data points
All of the training data points
Some of the cross validation data points
All of the cross validation data points
A SVM with a linear kernel is the same as an SVM with no kernel
*
1 point
True
False
What is a good "regime" for SVMs with Gaussian kernels (where N = number of features and M = number of training examples)?
*
1 point
M small, N very large
N small, M very large
N small, M is intermediate
I'd like to clarify / learn more about
What kernels are
What kernels are good for
How SVMs work
What "support vector" means
What "linearly separable" means
How SVMs deal with data that are not linearly separable
Other:
Submit
Never submit passwords through Google Forms.
This content is neither created nor endorsed by Google.
Report Abuse

Terms of Service
Forms