Richard Ehrenborg [1] has provided a nice magic trick that can be used to illustrate many properties of Hamming codes. His paper includes a set of manipulatives that can be used to implement the trick. An improved version of this Hamming code magic trick is described in a recent paper in Math Horizons [3]. In this paper, we introduce a similar magic trick that is based on a Reed–Solomon code. I...

Here we propose a novel machine learning method for time series forecasting which is based on the widelyused Least Squares Support Vector Machine (LS-SVM) approach. The objective function of our method contains a weighted variance minimization part as well. This modification makes the method more efficient in time series forecasting as this paper will show. The proposed method is a generalizati...

We introduce a framework for the recovery of points on a smooth surface in high-dimensional space, with application to dynamic imaging. We assume the surface to be the zero-level set of a bandlimited function. We show that the exponential maps of the points on the surface satisfy annihilation relations, implying that they lie in a finite dimensional subspace. We rely on nuclear norm minimizatio...

The relevance vector machine (RVM) (Tipping, 2001) encapsulates a sparse probabilistic model for machine learning tasks. Like support vector machines, use of the kernel trick allows modelling in high dimensional feature spaces to be achieved at low computational cost. However, sparsity is controlled not just by the automatic relevance determination (ARD) prior but also by the choice of basis fu...