; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…Continue
In an earlier blog, “Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation”, I introduced the need for Dynamical ML as we now enter the “Walk” stage of “Crawl-Walk-Run” evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two…Continue
Added by PG Madhavan on October 6, 2016 at 10:04am — No Comments
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…Continue
Added by PG Madhavan on September 18, 2016 at 8:00am — No Comments
In my recent blog, Marrying Kalman Filtering & Machine Learning, we saw the merger of Bayesian exact recursive estimation (algorithm for which is Kalman Filter/Smoother in the linear, Gaussian case) and Machine Learning. We developed a solution called Kernel Projection Kalman Filter for business applications that…Continue