Universal randomized switching

Abstract--In this paper, we consider a competitive approach to sequential decision problems, suitable for a variety of signal processing applications where at each of a succession of times, a selection must be made from among a fixed set of strategies (or outcomes). For each such decision and outcome pair, loss is incurred, and it is the time-accumulation of these losses that is sought to be minimized. Rather than using a statistical performance measure, our goal in this pursuit is to sequentially accumulate loss that is no larger than that of the best loss that could be obtained through a partitioning of the sequence of observations into an arbitrary fixed number of segments and independently selecting a different strategy for each segment. For this purpose, we introduce a randomized sequential algorithm built upon that of Kozat and Singer that asymptotically achieves the performance of a noncausal algorithm that would be able to choose the number of segments and the best algorithm fo...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Suleyman Serdar Kozat, Andrew C. Singer
• Keywords: Artificial Intelligence, Randomized Sequential Algorithm, Sequential Decision Problems, Signal Processing Applications, TSP 2010