CSC321:  Neural Networks

Lecture 11: Linear Support Vector Machines

Getting good generalization on big datasets

Preprocessing the input vectors

Is preprocessing cheating?

A hierarchy of model classes

A way to choose a model class

A weird measure of model complexity

An example of VC dimension

Some examples of VC dimension

The probabilistic guarantee

Preventing overfitting when using big sets of features

Support Vector Machines

Training a linear SVM

Testing a linear SVM

A Bayesian Interpretation

What to do if there is no separating plane

Introducing slack variables

A picture of the best plane with a slack variable