14_SVM - 10/28/2009 Outline Transform a linear learner into...

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10/28/2009 1 Support Vector Machines Outline Transform a linear learner into a non linear learner Kernels can make high dimensional spaces tractable Kernels can make non vectorial data tractable Non Linear Problems Problem: some tasks have non linear structure no hyperplane is sufficiently accurate How can SVMs learn non linear classification rules? Extending the Hypothesis Space Idea: add more features Learn linear rule in feature space. Example: The separating hyperplane in feature space is degree two polynomial in input space. Transformation Instead of x 1 , x 2 use
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10/28/2009 2 How do we find these features? F( x) = Reminder From http://www.support vector.net/icml tutorial.pdf Reminder From http://www.support vector.net/icml tutorial.pdf Observation From http://www.support vector.net/icml tutorial.pdf
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10/28/2009 3 Dual Representation From http://www.support vector.net/icml tutorial.pdf
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This note was uploaded on 05/30/2010 for the course CS 4700 taught by Professor Joachims during the Fall '07 term at Cornell University (Engineering School).

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14_SVM - 10/28/2009 Outline Transform a linear learner into...

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