Title :
Sequential Detection Using Least Squares Temporal Difference Methods
Author :
Kuh, Anthony ; Mandic, Danilo
Author_Institution :
Dept. of Electr. Eng., Hawaii Univ., Honolulu, HI
Abstract :
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
Keywords :
learning (artificial intelligence); least squares approximations; pattern recognition; decentralized detection problems; least squares temporal difference learning algorithm; sequential detection problems; training sequences; Algorithm design and analysis; Educational institutions; Learning; Least squares approximation; Least squares methods; Probability; Sequential analysis; Statistics; Testing; Training data;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661372
