.

** Dynamical ML is machine learning that can adapt to variations over time**; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…

Added by PG Madhavan on March 18, 2017 at 2:30pm — 1 Comment

** **

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…

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…

ContinueAdded 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…

Added by PG Madhavan on July 21, 2016 at 2:06pm — 1 Comment

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