The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel it...
Seung-Jean Kim, Argyrios Zymnis, Alessandro Magnan...
The application of kernel-based learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a...
Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pa...
Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Ro...
We review a multiple kernel learning (MKL) technique called p regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denois...
Regularized Least Squares (RLS) algorithms have the ability to avoid over-fitting problems and to express solutions as kernel expansions. However, we observe that the current RLS ...