The support vector machined kernel

In this paper, we propose the so-called “SVM'ed-kernel function” and its use in SVM classification problems. This kernel function is itself a support vector machine classifier that is learned statistically from data. We show that the new kernel manages to change the classical methodology of defining a feature vector for each pattern. One will only need to define features representing the similarity between two patterns allowing many details to be captured in a concise way. The new proposed kernel shows very promising results. It opens the door for new feature definitions that could be created in various machine learning problems where similarity between patterns can be formulated more suitably.