On epsilon-optimality of the pursuit learning algorithm

Details On epsilon-optimality of the pursuit learning algorithm On epsilon-optimality of the pursuit learning algorithm

2011-06-16

arxiv.org/abs/1106.3355v2

Computer Science

Learning

Scientific paper

Estimator algorithms in learning automata are useful tools for adaptive, real-time optimization in computer science and engineering applications. This paper investigates theoretical convergence properties for a special case of estimator algorithms: the pursuit learning algorithm. In this note, we identify and fill a gap in existing proofs of probabilistic convergence for pursuit learning. It is tradition to take the pursuit learning tuning parameter to be fixed in practical applications, but our proof sheds light on the importance of a vanishing sequence of tuning parameters in a theoretical convergence analysis.

Affiliated with

Also associated with

No associations

Rating

On epsilon-optimality of the pursuit learning algorithm does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On epsilon-optimality of the pursuit learning algorithm, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On epsilon-optimality of the pursuit learning algorithm will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-695242