| Paper: | MLSP-P2.4 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Sequential learning; sequential decision methods | ||
| Title: | Sequential Detection using Least Squares Temporal Difference Methods | ||
| Authors: | Anthony Kuh, University of Hawaii, United States; Danilo Mandic, Imperial College London, United Kingdom | ||
| 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. | ||
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