Composite Kernels for Hypertext Categorisation

Abstract

Kernels are problem-specific functions that act as an interface between the learning system and the data. While it is well-known when the combination of two kernels is again a valid kernel, it is an open question if the resulting kernel will perform well. In particular, in which situations can a combination of kernel be expected to perform better than its components considered separately? We investigate this problem by looking at the task of designing kernels for hypertext classification, where both words and links information can be exploited. We provide sufficient conditions that indicate when an improvement can be expected, highlighting and formalising the notion of "independent kernels". Experimental results confirm the predictions of the theory in the hypertext domain.