Barzilay and Brailovsky (1999)
"An approach to constructing a kernel function which takes into account some domain
knowledge about a problem and thus essentially diminishes the number of noisy parameters in high dimensional feature space is suggested."

Chapelle (2001) and Sch?o?lkopf
"The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transfrmation invariances."

Tax and Duin (1999)
"This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vector domain description (SVDD). This data description can be used for novelty or outlier detection. A spherically shaped decision boundary around a set of objects is constructed by a set of support vectors describing the sphere boundary. It has the possibility of transforming the data to new feature spaces without much extra computational cost. By using the transformed data, this SVDD can obtain more Řexible and more accurate data descriptions. The error of the first kind, the fraction of the training objects which will be rejected, can be estimated immediately from the description without the use of an independent test set, which makes this method data efficient. The support vector domain description is compared with other outlier detection methods on real data."