  A Support Vector Machine (SVM) has become a very popular machine learning
method for text classification. One reason for this relates to the range of
existing kernels which allow for classifying data that is not linearly
separable. The linear, polynomial and RBF (Gaussian Radial Basis Function)
kernel are commonly used and serve as a basis of comparison in our study. We
show how to derive the primal form of the quadratic Power Kernel (PK) -- also
called the Negative Euclidean Distance Kernel (NDK) -- by means of complex
numbers. We exemplify the NDK in the framework of text categorization using the
Dewey Document Classification (DDC) as the target scheme. Our evaluation shows
that the power kernel produces F-scores that are comparable to the reference
kernels, but is -- except for the linear kernel -- faster to compute. Finally,
we show how to extend the NDK-approach by including the Mahalanobis distance.
