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Effectiveness estimation of matrices compression in the procedures of randomized machine learning
Informacionnye tehnologii i vyčislitelnye sistemy, no. 1 (2018), pp. 3-7 Cet article a éte moissonné depuis la source Math-Net.Ru

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The method of estimation effectiveness of the matrices compressions. That oriented to the procedures randomized machine learning. It is proposed to measure of effectiveness in the term of the Kullback-Leibler function.
Keywords: randomized machine learning, entropy, KL-distance. 
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     author = {Yu. S. Popkov},
     title = {Effectiveness estimation of matrices compression in the procedures of randomized machine learning},
     journal = {Informacionnye tehnologii i vy\v{c}islitelnye sistemy},
     pages = {3--7},
     year = {2018},
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     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ITVS_2018_1_a0/}
}
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Yu. S. Popkov. Effectiveness estimation of matrices compression in the procedures of randomized machine learning. Informacionnye tehnologii i vyčislitelnye sistemy, no. 1 (2018), pp. 3-7. http://geodesic.mathdoc.fr/item/ITVS_2018_1_a0/