Zobrazeno 1 - 10 of 144 pro vyhledávání: '"Lev V. Utkin"'
1
Akademický článek
Uncertainty Interpretation of the Machine Learning Survival Model Predictions
Autor: Lev V. Utkin, Vladimir S. Zaborovsky, Maxim S. Kovalev, Andrei V. Konstantinov, Natalia A. Politaeva, Alexey A. Lukashin
Publikováno v: IEEE Access, Vol 9, Pp 120158-120175 (2021)
A method for interpreting uncertainty of predictions provided by machine learning survival models is proposed. It is called UncSurvEx and aims to determine which features of an analyzed example lead to uncertain predictions of an explainable black-bo
Externí odkaz: https://doaj.org/article/6c0edcc9b0e9490cb60ec34f81e6af05
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2
Akademický článek
Fuzzy One-Class Classification Model Using Contamination Neighborhoods
Autor: Lev V. Utkin
Publikováno v: Advances in Fuzzy Systems, Vol 2012 (2012)
A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by mini
Externí odkaz: https://doaj.org/article/02ad9b700d92482395591ffde9d6688f
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5
A New Adaptive Weighted Deep Forest and Its Modifications
Autor: Lev V. Utkin, Anna A. Meldo, Viacheslav S. Chukanov, Andrei V. Konstantinov
Publikováno v: International Journal of Information Technology & Decision Making. 19:963-986
A new adaptive weighted deep forest algorithm which can be viewed as a modification of the confidence screening mechanism is proposed. The main idea underlying the algorithm is based on adaptive weigting of every training instance at each cascade lev
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9025b045269caaf08f6a85f8cdcabb19
https://doi.org/10.1142/s0219622020500236
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6
Explanation of Siamese Neural Networks for Weakly Supervised Learning
Autor: Ernest M. Kasimov, Lev V. Utkin, Maxim S. Kovalev
Publikováno v: COMPUTING AND INFORMATICS; Vol. 39 No. 6 (2020): Computing and Informatics; 1172–1202
A new method for explaining the Siamese neural network (SNN) as a black-box model for weakly supervised learning is proposed under condition that the output of every subnetwork of the SNN is a vector which is accessible. The main problem of the expla
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eba6b4877e3a4128225df623bfcf14a6
https://doi.org/10.31577/cai_2020_6_1172
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7
Multi-Attention Multiple Instance Learning
Autor: Andrei V. Konstantinov, Lev V. Utkin
A new multi-attention based method for solving the MIL problem (MAMIL), which takes into account the neighboring patches or instances of each analyzed patch in a bag, is proposed. In the method, one of the attention modules takes into account adjacen
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5766631ca0b7aa64cf1ea6c78c967d70
http://arxiv.org/abs/2112.06071
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10
A weighted random survival forest
Autor: Lev V. Utkin, Mikhail A. Ryabinin, Andrei V. Konstantinov, Anna A. Meldo, Viacheslav S. Chukanov, Mikhail V. Kots
Publikováno v: Knowledge-Based Systems. 177:136-144
A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for e
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf574f738a658884452cd6ea0a85bc8b
https://doi.org/10.1016/j.knosys.2019.04.015
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