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In this paper we introduce a control policy which we refer to as the iterated approximate value function policy. The generation of this policy requires two stages, the first one carried out off-line, and the second stage carried out on-line. In the first stage we simultaneously compute a trajectory of moments of the state and action and a sequence of approximate value functions optimized to that trajectory...
We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as $(F_{\beta })$ score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines),...
Particle swarm optimization (PSO) has been proposed as an alternative to traditional evolutionary algorithms. In this work, we present a new PSO approach to control the trade-off between exploitation and exploration in the search process for solving multimodal functions. Our approach focuses on two search strategies for multimodal functions. One is a two-swarm cooperative strategy that controls search...
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