10-601B Kernelized SVMs
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Jump to navigationJump to searchThis a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Support Vector Machines: Bishop 7.1, Murphy 14.5
- Andrew Ng's notes on SVM optimization
What You Should Know Afterward
- The definitions of, and intuitions behind, these concepts:
- The margin of a classifier relative to a dataset.
- What a constrained optimization problem is.
- The primal form of the SVM optimization problem.
- The dual form of the SVM optimization problem.
- What a support vector is.
- What slack variables are and why and when they are used in SVMs.
- How to explain the different parts (constraints, optimization criteria) of the primal and dual forms for the SVM.
- How to Kernelize SVM