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This paper presents a comparative experimental study of the multidimensional indexing methods based on the approximation approach. We are particularly interested in the LSH family, which provides efficient index structures and solves the dimensionality curse problem. The goal is to understand the performance gain and the behavior of this family of methods(More)
The running time of the classical algorithms of the Markov Decision Process (MDP) typically grows linearly with the state space size, which makes them frequently intractable. This paper presents a Modified Policy Iteration algorithm to compute an optimal policy for large Markov decision processes in the discounted reward criteria and under infinite horizon.(More)
Many multidimensional hashing schemes have been actively studied in recent years, providing efficient nearest neighbor search. Generally, we can distinguish several hashing families, such as learning based hashing, which provides better hash function selectivity by learning the dataset distribution. The spacial hashing family proposes a suitable partition(More)
Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest Neighbours search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect search performances. The main(More)
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