Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel ...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
In this paper we propose a new criterion, based on Minimum Description Length (MDL), to estimate an optimal number of clusters. This criterion, called Kernel MDL (KMDL), is particu...
Ivan O. Kyrgyzov, Olexiy O. Kyrgyzov, Henri Ma&ici...
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...
This paper investigates the effect of Kernel Principal Component Analysis (KPCA) within the classification framework, essentially the regularization properties of this dimensional...
We present an algorithm for the computation of the discrete logarithm in logarithmic -Class Groups. This is applied to the calculation to the -rank of the wild kernel WK2 of a numb...