Lecture 15 - Stanford Vision Lab
Lecture 15 - Stanford Vision Lab
Lecture 15 - Stanford Vision Lab
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Nonlinear SVMs<br />
•Datasets that are linearly separable work out great:<br />
•<br />
•<br />
0 x<br />
•But what if the dataset is just too hard?<br />
0 x<br />
• We can map it to a higher‐dimensional space:<br />
x 2<br />
0 x<br />
Slide credit: Andrew Moore<br />
Fei-Fei Li<br />
<strong>Lecture</strong> <strong>15</strong> -<br />
37<br />
14‐Nov‐11