Current Slide

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

SVM—Support Vector Machines

  • A relatively new classification method for both linear and nonlinear data
  • It uses a nonlinear mapping to transform the original training data into a higher dimension
  • With the new dimension, it searches for the linear optimal separating hyperplane (i.e., “decision boundary”)
  • With an appropriate nonlinear mapping to a sufficiently high dimension, data from two classes can always be separated by a hyperplane
  • SVM finds this hyperplane using support vectors (“essential” training tuples) and margins (defined by the support vectors)

Speaker notes:

Content Tools

Sources

There are currently no sources for this slide.