This paper proposes a framework for single-image super-resolution. The underlying idea is
to learn a map from input low-resolution images to target high-resolution images based on …

In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is
proposed. Our approach relies on sparsely representing a test sample in terms of all of the …

We present a nonlinear version of the recursive least squares (RLS) algorithm. Our
algorithm performs linear regression in a high-dimensional feature space induced by a …

Over the last fifty years, the ability to carry out analysis as a precursor to decision making in
engineering design has increased dramatically. In particular, the advent of modern …

Machine learning develops intelligent computer systems that are able to generalize from
previously seen examples. A new domain of machine learning, in which the prediction must …

We compare machine learning methods applied to a difficult real-world problem: predicting
computer hard-drive failure using attributes monitored internally by individual drives. The …

We describe a feature selection method that can be applied directly to models that are linear
with respect to their parameters, and indirectly to others. It is independent of the target …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

Falls are a major public health concern and main causes of accidental death in the senior
US population. Timely and accurate detection permit immediate assistance after a fall and …

We describe twin Gaussian processes (TGP), a generic structured prediction method that
uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and …