The choice of the kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. The past few years have se...
In high-dimensional classification problems it is infeasible to include enough training samples to cover the class regions densely. Irregularities in the resulting sparse sample d...
We consider the sparse grid combination technique for regression, which we regard as a problem of function reconstruction in some given function space. We use a regularised least ...
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
In this work, we study several properties of the SHA-2 design which have been utilized in recent collision attacks against reduced round SHA-2. Small modifications to the SHA-2 des...