Support Vector Machines
We describe the central ideas of Support Vector (SV) learning in a nutshell. This includes the notion of learning from data via risk minimization and the notion of kernels as an efficient means of encoding prior information. We give an overview over the optimization problems arising from SV classification and discuss various extensions such as kernel PCA and automatic margin adaptation. 
 
 
Keywords: 
 
support vectors; 
kernels; 
feature space; 
regularization; 
risk minimization; 
optimization; 
classification