Guest Editorial: Special Issue on Machine Learning Methods in Signal Processing

MUCH of modern statistical and adaptive signal processing relies on learning algorithms of one form or another. While some of the rich literature on machine learning has penetrated the signal processing community in great depth, a variety of new techniques, which offer tremendous potential for signal processing applications, have not enjoyed similar broad acceptance in the signal processing community. Raising awareness and surveying the state of art of machine learning for signal processing applications and theory is both timely and important. This special issue is directed to both signal processing and machine learning researchers, based on our belief that great synergies can be realized by further stimulating the cross-fertilization that has been ongoing between these strong research communities. The scope of this Special Issue of the IEEE TRANSACTIONS