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A multi-Markovian switching-based strategy for solving the stochastic point location problem

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Abstract

Stochastic Point Location problem considering that a learning entity (i.e. mechanisms, algorithm, etc) attempts to locate a certain point by interaction with a stochastic environment is encountered widely in Machine Learning. A conventional technique is to sample the search space into discrete points and perform a random walk. Nevertheless, the random walk is confined to the neighboring point. In this paper, an extended version of the random walk-based triple level algorithm is introduced to overcome the aforementioned defect. Specifically, the proposed algorithm exploits the multi-Markovian switching to generalize the random walk concerning adjacent nodes to intermittent nodes. Hence, the whole approach could be regarded as the Markov chain, and its transform matrix could be constructed, followed by a rigorous mathematical pf procedure of the convergence. The experimental results demonstrate the effectiveness and efficiency of the proposed algorithm, showing its abilities of stronger stability, a higher precision, and a faster speed in comparison with the counterparts available in open literatures.

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Acknowledgements

This research work is funded by the Science Foundation of North China University of Technology 110051360002, the Basic Scientific Research from Beijing Education Commission 110052972027, and the National Nature Science Foundation of China under Grant 61971283.

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Authors and Affiliations

  1. Department of Computer Science, North China University of Technology, No. 5 Jinyuanzhuang Road, Shijingshan District, Beijing, 100144, People’s Republic of China

    Ying Guo

  2. Department of Electronic Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Min Hang District, Shanghai, 200240, People’s Republic of China

    Shenghong Li

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  1. Ying Guo
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  2. Shenghong Li
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Correspondence to Ying Guo.

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Guo, Y., Li, S. A multi-Markovian switching-based strategy for solving the stochastic point location problem. Neural Comput & Applic 34, 6825–6846 (2022). https://doi.org/10.1007/s00521-022-06894-2

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