Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
Abstract. It has been shown that many kernel methods can be equivalently formulated as minimal-enclosing-ball (MEB) problems in certain feature space. Exploiting this reduction eff...
Emanuele Frandi, Maria Grazia Gasparo, Stefano Lod...
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...