In many applied problems in the context of pattern recognition, the data often involve highly asymmetric observations. Normal mixture models tend to overfit when additional compone...
`Approximate message passing' algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive num...
A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbi...
Mu-Chun Su, Chien-Hsing Chou, Eugene Lai, Jonathan...
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same clu...
This paper presents a computational approach for the frequency-domain identification of multivariable, discrete-time transfer function models based on a cost function minimization...