SVM optimality conditions

(1.1 hours to learn)


Using Lagrange duality, we can formulate a set of conditions that characterize the optimal solution to the SVM objective. These conditions show that the weight vector is a linear combination of a (hopefully small) subset of the training points, those for which the margin constraint is tight.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Stanford's Machine Learning lecture notes
Lecture notes for Stanford's machine learning course, aimed at graduate and advanced undergraduate students.
Author: Andrew Y. Ng

Supplemental resources (the following are optional, but you may find them useful)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman


See also

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