Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of the input space. Recently there has been a growing interest in algorithms that ...
The dual-tree wavelet transform is here applied to the problem of fractal dimension estimation. The Hurst parameter of fractional Brownian surfaces is estimated using various wave...
The goal of sufficient dimension reduction in supervised learning is to find the lowdimensional subspace of input features that is `sufficient' for predicting output values. ...
In this paper we shall derive a posteriori error estimates in the L1-norm for upwind finite volume schemes for the discretization of nonlinear conservation laws on unstructured gri...
We give bit-size estimates for the coefficients appearing in triangular sets describing positive-dimensional algebraic sets defined over Q. These estimates are worst case upper bo...