Abstract
The existing strategies on Stochastic Point Location (SPL) are adopted to search a point or parameter on a real line under stochastic environment, which have been demonstrated to be effective. However, one problem which still has not been addressed yet is how to learn a point in multidimensional space under stochastic environment. This problem will become more difficult when the environment itself is a deceptive one in which the probability (p) of the correct response emitted from the environment is p < 0.5. In this paper, a general framework is proposed to deal with all the above-mentioned problems. A key aspect of our method worth mentioning is that it can transform learning a point in space into one of searching d optimal points on d different super lines, where d is the dimension size. Finally, the excellent performance of our proposed algorithm has been proved by our rigorous mathematical proof and validated by a great number of experiments.
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Jiang, W., Yan, Y., Ge, H., Li, S. (2016). General Framework for Parameter Learning and Optimization in Stochastic Environments. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds) Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49831-6_107
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DOI: https://doi.org/10.1007/978-3-662-49831-6_107
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