Machine Learning week 7 quiz: Support Vector Machines
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Support Vector Machines
5 试题
Suppose you have trained an SVM classifier with a Gaussian kernel, and it learned the following decision boundary on the training set:
When you measure the SVM's performance on a cross validation set, it does poorly. Should you try increasing or decreasing
It would be reasonable to try decreasing
It would be reasonable to try increasing
It would be reasonable to try decreasing
It would be reasonable to try increasing
The formula for the Gaussian kernel is given by
The figure below shows a plot of
Which of the following is a plot of
Figure 3.
Figure 2.
Figure 4.
The SVM solves
where the functions
The first term in the objective is:
This first term will be zero if two of the following four conditions hold true. Which are the two conditions that would guarantee that this term equals zero?
For every example with
For every example with
For every example with
For every example with
Suppose you have a dataset with n = 10 features and m = 5000 examples.
After training your logistic regression classifier with gradient descent, you find that it has underfit the training set and does not achieve the desired performance on the training or cross validation sets.
Which of the following might be promising steps to take? Check all that apply.
Use an SVM with a linear kernel, without introducing new features.
Use an SVM with a Gaussian Kernel.
Create / add new polynomial features.
Increase the regularization parameter
Which of the following statements are true? Check all that apply.
Suppose you are using SVMs to do multi-class classification and
would like to use the one-vs-all approach. If you have
classes, you will train
The maximum value of the Gaussian kernel (i.e.,
If the data are linearly separable, an SVM using a linear kernel will
return the same parameters
It is important to perform feature normalization before using the Gaussian kernel.
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