: A new supervised learning procedure for training RBF networks is proposed. It uses a pair of parallel running Kalman filters to sequentially update both the output weights and th...
Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the...
Mattias Rantalainen, Olivier Cloarec, Timothy M. D...
Forecasting sequences by expert ensembles generally assumes stationary or near-stationary processes; however, in complex systems and many real-world applications, we are frequentl...
Cosma Rohilla Shalizi, Abigail Z. Jacobs, Aaron Cl...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...
Abstract. We introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-...