Information evolution of optimal learning

author: Roman V. Belavkin, School of Computing Science, Middlesex University
published: Sept. 4, 2008,   recorded: May 2008,   views: 5425


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


It is widely accepted that learning is closely related to theories of optimisation and information. Indeed, there is no need to learn if there is nothing to optimise; if one possesses full information, then there is simply nothing new to learn. The paper considers learning as an optimisation problem with dynamical information constraints. Unlike the standard approach in the optimal control theory, where the solutions are given by the Hamilton–Jacobi–Bellman equation for Markov time evolution, the optimal solution is presented as the system of canonical Euler equations defining the optimal information–utility trajectory in the conjugate space. The optimal trajectory is parameterised by theinformation–utility constraints, which are illustrated on examples for finite and infinite–dimensional cases.

See Also:

Download slides icon Download slides: aispds08_belavkin_ieol_01.v1.pdf (882.2 KB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: