Geodesic
Power sum kernels in permutation learning
Zapiski Nauchnykh Seminarov POMI, Probability and statistics. Part 34, Tome 525 (2023), pp. 7-21

Voir la notice de l'article provenant de la source Math-Net.Ru

In this paper, we consider the use of power sum kernels in solving the problem of permutation learning. We present a way to approximate a symmetrized kernel that naturally arises in this problem using the Monte Carlo method and estimate the convergence rate. We also touch on the problem of partial rankings and present some results for the case when the number of fixed elements is 1 or 2. 
@article{ZNSL_2023_525_a1,
     author = {I. F. Azangulov and D. A. Eremeev},
     title = {Power sum kernels in permutation learning},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {7--21},
     publisher = {mathdoc},
     volume = {525},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2023_525_a1/}
}
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I. F. Azangulov; D. A. Eremeev. Power sum kernels in permutation learning. Zapiski Nauchnykh Seminarov POMI, Probability and statistics. Part 34, Tome 525 (2023), pp. 7-21. http://geodesic.mathdoc.fr/item/ZNSL_2023_525_a1/