In Jutten’s blind separation algorithm, symmetrical distribution and statistical independence of the signal sources are assumed. When they are not satisfied, the learning proce...
This paper presents a new adaptive procedure for the linear and non-linear separation of signals with non-uniform, symmetrical probability distributions, based on both simulated a...
In recent years analysis of complexity of learning Gaussian mixture models from sampled data has received significant attention in computational machine learning and theory commun...
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Abstract. We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kear...