## Kernel Methods for Classification

Kernel Methods approach the problem by mapping the data into a high dimensional feature space, where each coordinate corresponds to one feature of the data items, transforming the data into a set of points in a Euclidean space. In that space, a variety of methods can be used to find relations in the data. Since the mapping can be quite general (not necessarily linear, for example), the relations found in this way are accordingly very general. This approach is called the kernel trick.
More information on Wikipedia.
The model information displays Support Vectors as black or white circles. When both black and white circles are shown, black circles correspond to SVs that fall inside or beyond the SVM margin.
The ROC curve is generated by varying the threshold on the output function of the SVM/RVM classifier (whereas by default this threshold is set to 0 and the classification function is the sign(x) function).
Kernel Parameters:

- Kernel Type:

- Linear: linear kernel
- Polynomial: polynomial kernel
- RBF: radial basis function (gaussian) kernel

- Kernel Width: inverse variance for the kernel function, determines the radius of influence of each sample (RBF + Poly)
- Degree: degree of the polynomial (Poly)

Methods:

- C-SVM: Support Vector Machine

- C: penalty parameter (computes nu automatically)

- nu-SVM: Support Vector Machine

- nu: fraction of support vectors to be kept (computes C automatically)

- RVM: Relevance Vector Machine

- eps: convergence threshold

- Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

- lambda: convergence threshold
- maxSV: maximum number of support vector allowed