**CSC 2515 2008
Lecture 10
Support Vector Machines**

**Getting good generalization
on big datasets**

**Preprocessing the input
vectors**

**A weird measure of model
complexity**

**Preventing overfitting when
using big sets of features**

**What to do if there is no
separating plane**

**A picture of the best plane
with a slack variable**

**Why do large margin
separators have lower VC dimension?**

**A potential problem and a
magic solution**

**What the kernel trick
achieves**

**Support Vector Machines are
Perceptrons!**

**Learning to extract the
orientation of a face patch (Ruslan Salakhutdinov)**

**The root mean squared error
in the orientation when combining GP’s with deep belief nets**