In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow...
Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshe...
Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
Qualitative assessment of scientific computations is an emerging application area that applies a data-driven approach to characterize, at a high level, phenomena including conditi...
Frequency hopping (FH) signals have well-documented merits for commercial and military applications due to their near-far resistance and robustness to jamming. Estimating FH signa...
Daniele Angelosante, Georgios B. Giannakis, Nichol...