Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDLev V. Utkin
Person information
- affiliation: Peter the Great St. Petersburg Polytechnic University, Russia
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j78]Stanislav Kirpichenko
, Lev V. Utkin, Andrei V. Konstantinov, Vladimir Muliukha:
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. Algorithms 17(1): 40 (2024)- [i35]Andrei V. Konstantinov, Boris V. Kozlov, Stanislav R. Kirpichenko, Lev V. Utkin:
Dual feature-based and example-based explanation methods. CoRR abs/2401.16294 (2024) - [i34]Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin:
Generating Survival Interpretable Trajectories and Data. CoRR abs/2402.12331 (2024) - [i33]Andrei V. Konstantinov, Lev V. Utkin:
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning. CoRR abs/2402.14726 (2024) - [i32]Rinat I. Dumaev, Sergei A. Molodyakov, Lev V. Utkin:
Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images. CoRR abs/2405.17483 (2024) - 2023
- [j77]Lev V. Utkin, Andrey Y. Ageev, Andrei V. Konstantinov, Vladimir Muliukha:
Improved Anomaly Detection by Using the Attention-Based Isolation Forest. Algorithms 16(1): 19 (2023) - [j76]Andrei V. Konstantinov, Stanislav Kirpichenko, Lev V. Utkin:
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression. Algorithms 16(5): 226 (2023) - [j75]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha:
Multiple Instance Learning with Trainable Soft Decision Tree Ensembles. Algorithms 16(8): 358 (2023) - [j74]Andrei V. Konstantinov, Lev V. Utkin:
Attention-like feature explanation for tabular data. Int. J. Data Sci. Anal. 16(1): 1-26 (2023) - [j73]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha:
LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models. Informatics 10(2): 40 (2023) - [j72]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable ensembles of hyper-rectangles as base models. Neural Comput. Appl. 35(29): 21771-21795 (2023) - [j71]Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko:
Attention and self-attention in random forests. Prog. Artif. Intell. 12(3): 257-273 (2023) - [c19]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha, Vladimir S. Zaborovsky:
GBMILs: Gradient Boosting Models for Multiple Instance Learning. ICR 2023: 233-245 - [i31]Andrei V. Konstantinov, Lev V. Utkin:
Multiple Instance Learning with Trainable Decision Tree Ensembles. CoRR abs/2302.06601 (2023) - [i30]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable Ensembles of Hyper-Rectangles as Base Models. CoRR abs/2303.08625 (2023) - [i29]Andrei V. Konstantinov, Lev V. Utkin, Alexey Lukashin, Vladimir Muliukha:
Neural Attention Forests: Transformer-Based Forest Improvement. CoRR abs/2304.05980 (2023) - [i28]Andrei V. Konstantinov, Lev V. Utkin:
A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints. CoRR abs/2307.10459 (2023) - [i27]Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov:
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator. CoRR abs/2308.03730 (2023) - [i26]Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov:
SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models. CoRR abs/2312.06638 (2023) - 2022
- [j70]Lev V. Utkin, Andrei V. Konstantinov:
Ensembles of Random SHAPs. Algorithms 15(11): 431 (2022) - [j69]Andrei V. Konstantinov, Lev V. Utkin:
Multi-attention multiple instance learning. Neural Comput. Appl. 34(16): 14029-14051 (2022) - [j68]Lev V. Utkin, Egor D. Satyukov, Andrei V. Konstantinov:
SurvNAM: The machine learning survival model explanation. Neural Networks 147: 81-102 (2022) - [j67]Lev V. Utkin, Andrei V. Konstantinov:
Attention-based random forest and contamination model. Neural Networks 154: 346-359 (2022) - [c18]Andrei V. Konstantinov, Lev V. Utkin, Stanislav Kirpichenko:
AGBoost: Attention-based Modification of Gradient Boosting Machine. FRUCT 2022: 96-101 - [c17]Andrei V. Konstantinov, Lev V. Utkin:
Multiple Instance Learning through Explanation by Using a Histopathology Example. FRUCT 2022: 102-108 - [i25]Lev V. Utkin, Andrei V. Konstantinov:
Attention-based Random Forest and Contamination Model. CoRR abs/2201.02880 (2022) - [i24]Lev V. Utkin, Andrei V. Konstantinov:
Attention and Self-Attention in Random Forests. CoRR abs/2207.04293 (2022) - [i23]Andrei V. Konstantinov, Lev V. Utkin, Stanislav Kirpichenko:
AGBoost: Attention-based Modification of Gradient Boosting Machine. CoRR abs/2207.05724 (2022) - [i22]Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin:
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression. CoRR abs/2207.09139 (2022) - [i21]Lev V. Utkin, Andrey Y. Ageev, Andrei V. Konstantinov:
Improved Anomaly Detection by Using the Attention-Based Isolation Forest. CoRR abs/2210.02558 (2022) - [i20]Andrei V. Konstantinov, Lev V. Utkin:
LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models. CoRR abs/2210.05168 (2022) - [i19]Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov:
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. CoRR abs/2211.10793 (2022) - 2021
- [j66]Lev V. Utkin, Vladimir S. Zaborovsky, Maxim S. Kovalev, Andrei V. Konstantinov, Natalia A. Politaeva, Alexey Lukashin:
Uncertainty Interpretation of the Machine Learning Survival Model Predictions. IEEE Access 9: 120158-120175 (2021) - [j65]Maxim Kovalev, Lev V. Utkin, Frank P. A. Coolen, Andrei V. Konstantinov:
Counterfactual Explanation of Machine Learning Survival Models. Informatica 32(4): 817-847 (2021) - [j64]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable machine learning with an ensemble of gradient boosting machines. Knowl. Based Syst. 222: 106993 (2021) - [c16]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha:
Gradient Boosting Machine with Partially Randomized Decision Trees. FRUCT 2021: 167-173 - [c15]Lev V. Utkin, Pavel D. Drobintsev, Maxim Kovalev, Andrei V. Konstantinov:
Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions. FRUCT 2021: 489-494 - [i18]Lev V. Utkin, Andrei V. Konstantinov:
Ensembles of Random SHAPs. CoRR abs/2103.03302 (2021) - [i17]Lev V. Utkin, Egor D. Satyukov, Andrei V. Konstantinov:
SurvNAM: The machine learning survival model explanation. CoRR abs/2104.08903 (2021) - [i16]Lev V. Utkin, Andrei V. Konstantinov, Kirill A. Vishniakov:
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data. CoRR abs/2106.09111 (2021) - [i15]Andrei V. Konstantinov, Lev V. Utkin:
Attention-like feature explanation for tabular data. CoRR abs/2108.04855 (2021) - [i14]Andrei V. Konstantinov, Lev V. Utkin:
Multi-Attention Multiple Instance Learning. CoRR abs/2112.06071 (2021) - 2020
- [j63]Anna A. Meldo, Lev V. Utkin, Maxim Kovalev, Ernest M. Kasimov:
The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. Artif. Intell. Medicine 108: 101952 (2020) - [j62]Lev V. Utkin, Maxim S. Kovalev, Frank P. A. Coolen:
Imprecise weighted extensions of random forests for classification and regression. Appl. Soft Comput. 92: 106324 (2020) - [j61]Lev V. Utkin, Maxim Kovalev, Ernest M. Kasimov:
Explanation of Siamese Neural Networks for Weakly Supervised Learning. Comput. Informatics 39(6) (2020) - [j60]Lev V. Utkin:
An imprecise deep forest for classification. Expert Syst. Appl. 141 (2020) - [j59]Lev V. Utkin, Mikhail V. Kots, Viacheslav S. Chukanov, Andrei V. Konstantinov, Anna A. Meldo:
Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. Int. J. Artif. Intell. Tools 29(5): 2050005:1-2050005:23 (2020) - [j58]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Anna A. Meldo:
A New Adaptive Weighted Deep Forest and Its Modifications. Int. J. Inf. Technol. Decis. Mak. 19(4): 963-986 (2020) - [j57]Lev V. Utkin, Kirill D. Zhuk:
Improvement of the Deep Forest Classifier by a Set of Neural Networks. Informatica (Slovenia) 44(1) (2020) - [j56]Maxim S. Kovalev, Lev V. Utkin, Ernest M. Kasimov:
SurvLIME: A method for explaining machine learning survival models. Knowl. Based Syst. 203: 106164 (2020) - [j55]Maxim S. Kovalev, Lev V. Utkin:
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds. Neural Networks 132: 1-18 (2020) - [c14]Vladimir Muliukha, Alexey Lukashin, Lev V. Utkin, Mikhail Popov, Anna A. Meldo:
Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System. ESANN 2020: 251-256 - [i13]Maxim S. Kovalev, Lev V. Utkin, Ernest M. Kasimov:
SurvLIME: A method for explaining machine learning survival models. CoRR abs/2003.08371 (2020) - [i12]Maxim S. Kovalev, Lev V. Utkin:
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds. CoRR abs/2005.02249 (2020) - [i11]Lev V. Utkin, Maxim S. Kovalev, Ernest M. Kasimov:
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models. CoRR abs/2005.02387 (2020) - [i10]Andrei V. Konstantinov, Lev V. Utkin:
Gradient boosting machine with partially randomized decision trees. CoRR abs/2006.11014 (2020) - [i9]Maxim S. Kovalev, Lev V. Utkin:
Counterfactual explanation of machine learning survival models. CoRR abs/2006.16793 (2020) - [i8]Andrei V. Konstantinov, Lev V. Utkin:
A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines. CoRR abs/2010.06026 (2020) - [i7]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines. CoRR abs/2010.07388 (2020)
2010 – 2019
- 2019
- [j54]Lev V. Utkin, Mikhail A. Ryabinin:
Discriminative Metric Learning with Deep Forest. Int. J. Artif. Intell. Tools 28(2): 1950007:1-1950007:19 (2019) - [j53]Lev V. Utkin:
An imprecise extension of SVM-based machine learning models. Neurocomputing 331: 18-32 (2019) - [j52]Lev V. Utkin, Maxim Kovalev, Anna A. Meldo:
A deep forest classifier with weights of class probability distribution subsets. Knowl. Based Syst. 173: 15-27 (2019) - [j51]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Mikhail A. Ryabinin, Anna A. Meldo:
A weighted random survival forest. Knowl. Based Syst. 177: 136-144 (2019) - [c13]Anna A. Meldo, Lev V. Utkin, Alexey Lukashin, Vladimir Muliukha, Vladimir S. Zaborovsky:
Database Acquisition for the Lung Cancer Computer Aided Diagnostic Systems. FRUCT 2019: 220-227 - [c12]Lev V. Utkin, Anna A. Meldo, Maxim Kovalev, Ernest M. Kasimov:
An Ensemble of Triplet Neural Networks for Differential Diagnostics of Lung Cancer. FRUCT 2019: 346-352 - [c11]Lev V. Utkin, Andrei V. Konstantinov, Anna A. Meldo, Mikhail A. Ryabinin, Viacheslav S. Chukanov:
A Deep Forest Improvement by Using Weighted Schemes. FRUCT 2019: 451-456 - [c10]Lev V. Utkin, Maxim Kovalev, Anna A. Meldo, Frank P. A. Coolen:
Imprecise Extensions of Random Forests and Random Survival Forests. ISIPTA 2019: 404-413 - [i6]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Mikhail A. Ryabinin, Anna A. Meldo:
A weighted random survival forest. CoRR abs/1901.00213 (2019) - [i5]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Anna A. Meldo:
An Adaptive Weighted Deep Forest Classifier. CoRR abs/1901.01334 (2019) - [i4]Lev V. Utkin, Mikhail V. Kots, Viacheslav S. Chukanov:
Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. CoRR abs/1909.03894 (2019) - [i3]Lev V. Utkin, Maxim S. Kovalev, Ernest M. Kasimov:
An explanation method for Siamese neural networks. CoRR abs/1911.07702 (2019) - 2018
- [j50]Lev V. Utkin, Yulia A. Zhuk:
A modification of the Lasso method by using the Bahadur representation for the genome-wide association study. Informatica (Slovenia) 42(2) (2018) - [j49]Lev V. Utkin, Mikhail A. Ryabinin:
A Siamese Deep Forest. Knowl. Based Syst. 139: 13-22 (2018) - [j48]Lev V. Utkin, Frank P. A. Coolen:
A robust weighted SVR-based software reliability growth model. Reliab. Eng. Syst. Saf. 176: 93-101 (2018) - 2017
- [j47]Sergey V. Gurov, Lev V. Utkin:
Reliability of repairable reserved systems with failure aftereffect. Autom. Remote. Control. 78(1): 113-124 (2017) - [j46]Lev V. Utkin, Vladimir S. Zaborovsky, Sergey G. Popov:
Siamese neural network for intelligent information security control in multi-robot systems. Autom. Control. Comput. Sci. 51(8): 881-887 (2017) - [j45]Lev V. Utkin, Yulia A. Zhuk:
Interval SVM-Based Classification Algorithm Using the Uncertainty Trick. Int. J. Artif. Intell. Tools 26(4): 1750014:1-1750014:32 (2017) - [j44]Lev V. Utkin, Yulia A. Zhuk:
An one-class classification support vector machine model by interval-valued training data. Knowl. Based Syst. 120: 43-56 (2017) - [i2]Lev V. Utkin, Mikhail A. Ryabinin:
A Siamese Deep Forest. CoRR abs/1704.08715 (2017) - [i1]Lev V. Utkin, Mikhail A. Ryabinin:
Discriminative Metric Learning with Deep Forest. CoRR abs/1705.09620 (2017) - 2016
- [j43]Lev V. Utkin, V. S. Zaborovskii, Sergey G. Popov:
Detection of anomalous behavior in a robot system based on deep learning elements. Autom. Control. Comput. Sci. 50(8): 726-733 (2016) - [j42]Lev V. Utkin, Anatoly I. Chekh, Yulia A. Zhuk:
Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels. Neural Networks 80: 53-66 (2016) - 2015
- [j41]Lev V. Utkin, Yulia A. Zhuk:
Robust Classifiers Using Imprecise Probability Models and Importance of Classes. Int. J. Artif. Intell. Tools 24(1): 1550008:1-1550008:28 (2015) - [j40]Lev V. Utkin:
The imprecise Dirichlet model as a basis for a new boosting classification algorithm. Neurocomputing 151: 1374-1383 (2015) - [j39]Lev V. Utkin, Andrea Wiencierz:
Improving over-fitting in ensemble regression by imprecise probabilities. Inf. Sci. 317: 315-328 (2015) - [j38]Lev V. Utkin, Anatoly I. Chekh:
A new robust model of one-class classification by interval-valued training data using the triangular kernel. Neural Networks 69: 99-110 (2015) - [j37]Lev V. Utkin, Frank P. A. Coolen, Sergey V. Gurov:
Imprecise inference for warranty contract analysis. Reliab. Eng. Syst. Saf. 138: 31-39 (2015) - 2014
- [j36]Lev V. Utkin, Yulia A. Zhuk:
Imprecise prior knowledge incorporating into one-class classification. Knowl. Inf. Syst. 41(1): 53-76 (2014) - [j35]Lev V. Utkin, Yulia A. Zhuk:
Robust boosting classification models with local sets of probability distributions. Knowl. Based Syst. 61: 59-75 (2014) - [j34]Lev V. Utkin:
A framework for imprecise robust one-class classification models. Int. J. Mach. Learn. Cybern. 5(3): 379-393 (2014) - [c9]Lev V. Utkin, Yulia A. Zhuk, Anatoly I. Chekh:
A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy. MLDM 2014: 107-118 - 2013
- [j33]Lev V. Utkin, Yulia A. Zhuk:
Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features. Adv. Artif. Intell. 2013: 176890:1-176890:12 (2013) - [j32]Lev V. Utkin, Yulia A. Zhuk:
Robust novelty detection in the framework of a contamination neighbourhood. Int. J. Intell. Inf. Database Syst. 7(3): 205-224 (2013) - [j31]Lev V. Utkin, Yulia A. Zhuk:
Fuzzy decision making using the imprecise Dirichlet model. Int. J. Math. Oper. Res. 5(1): 74-90 (2013) - [j30]Lev V. Utkin:
An Imprecise Boosting-like Approach to Classification. Int. J. Pattern Recognit. Artif. Intell. 27(8) (2013) - 2012
- [j29]Lev V. Utkin:
Fuzzy One-Class Classification Model Using Contamination Neighborhoods. Adv. Fuzzy Syst. 2012: 984325:1-984325:10 (2012) - [j28]Lev V. Utkin, Yulia A. Zhuk:
Combining of judgments in imprecise voting multi-criteria decision problems. Int. J. Appl. Decis. Sci. 5(3): 199-214 (2012) - [j27]Lev V. Utkin, Yulia A. Zhuk:
A machine learning algorithm for classification under extremely scarce information. Int. J. Data Anal. Tech. Strateg. 4(2): 115-133 (2012) - [j26]Lev V. Utkin, Natalia V. Simanova:
The DS/AHP Method under Partial Information about Criteria and Alternatives by Several Levels of Criteria. Int. J. Inf. Technol. Decis. Mak. 11(2): 307-326 (2012) - [c8]Van Hieu Nguyen, Lev V. Utkin, Dang Duy Thang:
A Pessimistic Approach for Solving a Multi-criteria Decision Making. KSE 2012: 121-127 - 2011
- [r1]Frank P. A. Coolen, Lev V. Utkin:
Imprecise Reliability. International Encyclopedia of Statistical Science 2011: 649-650 - 2010
- [j25]Lev V. Utkin:
Regression analysis using the imprecise Bayesian normal model. Int. J. Data Anal. Tech. Strateg. 2(4): 356-372 (2010)
2000 – 2009
- 2009
- [j24]Lev V. Utkin:
A new ranking procedure by incomplete pairwise comparisons using preference subsets. Intell. Data Anal. 13(2): 229-241 (2009) - [j23]Lev V. Utkin, Sébastien Destercke:
Computing expectations with continuous p-boxes: Univariate case. Int. J. Approx. Reason. 50(5): 778-798 (2009) - 2007
- [j22]Lev V. Utkin:
Second-order uncertainty calculations by using the imprecise Dirichlet model. Intell. Data Anal. 11(3): 225-244 (2007) - [j21]Lev V. Utkin, Thomas Augustin:
Decision making under incomplete data using the imprecise Dirichlet model. Int. J. Approx. Reason. 44(3): 322-338 (2007) - [j20]Lev V. Utkin:
Risk Analysis under Partial Prior Information and Nonmonotone Utility Functions. Int. J. Inf. Technol. Decis. Mak. 6(4): 625-647 (2007) - [p1]Lev V. Utkin, Frank P. A. Coolen:
Imprecise reliability: An introductory overview. Intelligence in Reliability Engineering 2007: 261-306 - 2006
- [j19]Lev V. Utkin:
Ranking procedures by pairwise comparison using random sets and the imprecise Dirichlet model. Appl. Math. Comput. 183(1): 394-408 (2006) - [j18]Lev V. Utkin:
A method for processing the unreliable expert judgments about parameters of probability distributions. Eur. J. Oper. Res. 175(1): 385-398 (2006) - [j17]Lev V. Utkin:
Cautious Analysis of Project Risks by Interval-Valued Initial Data. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 14(6): 663-685 (2006) - 2005
- [j16]Lev V. Utkin:
Comments on the paper "A behavioural model for vague probability assessments" by Gert de Cooman. Fuzzy Sets Syst. 154(3): 367-369 (2005) - [j15]Lev V. Utkin:
Extensions of belief functions and possibility distributions by using the imprecise Dirichlet model. Fuzzy Sets Syst. 154(3): 413-431 (2005) - [j14]Igor Kozine, Lev V. Utkin:
Constructing imprecise probability distributions. Int. J. Gen. Syst. 34(4): 401-408 (2005) - [j13]Lev V. Utkin:
Imprecise second-order model for a system of independent random variables. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 13(2): 177-193 (2005) - [j12]Lev V. Utkin, Igor Kozine:
Computing System Reliability Given Interval-Valued Characteristics of the Components. Reliab. Comput. 11(1): 19-34 (2005) - [c7]Lev V. Utkin, Thomas Augustin:
Powerful algorithms for decision making under partial prior information and general ambiguity attitudes. ISIPTA 2005: 349-358 - [c6]Lev V. Utkin, Thomas Augustin:
Decision making under incomplete data using the imprecise Dirichlet model. ISIPTA 2005: 359-368 - 2004
- [j11]Lev V. Utkin:
Reliability models of m-out-of-n systems under incomplete information. Comput. Oper. Res. 31(10): 1681-1702 (2004) - [j10]Lev V. Utkin:
Interval reliability of typical systems with partially known probabilities. Eur. J. Oper. Res. 153(3): 790-802 (2004) - [j9]Igor Kozine, Lev V. Utkin:
An approach to combining unreliable pieces of evidence and their propagation in a system response analysis. Reliab. Eng. Syst. Saf. 85(1-3): 103-112 (2004) - [j8]Lev V. Utkin:
A new efficient algorithm for computing the imprecise reliability of monotone systems. Reliab. Eng. Syst. Saf. 86(3): 179-190 (2004) - 2003
- [j7]Lev V. Utkin:
Imprecise Second-Order Hierarchical Uncertainty Model . Int. J. Uncertain. Fuzziness Knowl. Based Syst. 11(3): 301-318 (2003) - [j6]Lev V. Utkin:
A second-order uncertainty model for calculation of the interval system reliability. Reliab. Eng. Syst. Saf. 79(3): 341-351 (2003) - [c5]Lev V. Utkin:
A Second-Order Uncertainty Model of Independent Random Variables: An Example of the Stress-Strength Reliability. ISIPTA 2003: 530-544 - [c4]Lev V. Utkin:
Decision Making with Imprecise Second-Order Probabilities. ISIPTA 2003: 545-559 - 2002
- [j5]Lev V. Utkin, Igor Kozine:
Stress-strength reliability models under incomplete information. Int. J. Gen. Syst. 31(6): 549-568 (2002) - [j4]Igor Kozine, Lev V. Utkin:
Interval-Valued Finite Markov Chains. Reliab. Comput. 8(2): 97-113 (2002) - 2001
- [c3]Igor Kozine, Lev V. Utkin:
Different faces of the natural extension. ISIPTA 2001: 316-323 - [c2]Igor Kozine, Lev V. Utkin:
Computing the reliability of complex systems. ISIPTA 2001: 324-331
1990 – 1999
- 1999
- [j3]Lev V. Utkin, Sergey V. Gurov:
Imprecise Reliability of General Structures. Knowl. Inf. Syst. 1(4): 459-480 (1999) - [c1]Lev V. Utkin, Sergey V. Gurov:
Imprecise Reliability Models for the General Lifetime Distribution Classes. ISIPTA 1999: 333-342 - 1998
- [j2]Lev V. Utkin, Sergey V. Gurov:
Steady-state reliability of repairable systems by combined probability and possibility assumptions. Fuzzy Sets Syst. 97(2): 193-202 (1998) - 1996
- [j1]Lev V. Utkin, Sergey V. Gurov:
A general formal approach for fuzzy reliability analysis in the possibility context. Fuzzy Sets Syst. 83(2): 203-213 (1996)
Coauthor Index
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-06-25 20:59 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
