CS 451 Quiz 14
Support Vector Machines
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The cost function used in SVMs is similar to the cost function in logistic regression, but instead of using the log function
In logistic regression, we have a parameter lambda controlling the amount of regularization. In SVMs, what do we have instead?
While the hypothesis of logistic regression can be interpreted as a probability (a real number between 0 and 1), the hypothesis of an SVM is always either 0 or 1.
Support vector machines are also know as
In the context of SVMs, "margin" refers to
If we have data that is not linearly separable, then SVMs cannot be employed successfully
For any two nx1 vectors u and v, u' * v = v' * u (where u' denotes the transpose of u)
Which spelling mistake does Andrew Ng make in one of the videos?
To gain intuition about SVM optimization we consider the projections p(i) of the data points onto the feature vector theta and show that
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