Generalized Dynamical Machine Learning

In this year of Rudolf Kalman's demise, this article is dedicated to his memory. We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same method can be used for classification and regression (including forecasting), offline and real-time applications and simple and hard ML problems. We have achieved our objective in the form of State-space Recurrent Kernel-projection Time-varying Kalman or "RKT-Kalman" method.