Sequential Detection Using Least Squares Temporal Difference Methods
Anthony Kuh, Danilo P. Mandic · 2006
This paper considers sequential detection problems where we learn from sets of training sequences. The sufficient statistics can be learned quickly using a least squares temporal difference (TD) learning algorithm. This algorithm converges much quicker than previously applied TD learning algorithms. The algorithm can easily be implemented in an on-line manner and can also be applied to more complicated decentralized detection problems