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Evgueni N. Smirnov
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- affiliation (PhD): Maastricht University, Department of Data Science & Knowledge Engineering, The Netherlands
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Journal Articles
- 2020
- [j13]Alex Gammerman, Vladimir Vovk
, Zhiyuan Luo, Evgueni N. Smirnov, Ralf Peeters
Special Issue on Conformal and Probabilistic Prediction with Applications. Neurocomputing 397: 264-265 (2020) - [j12]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers, Ralf Peeters
Conformal Feature-Selection Wrappers and ensembles for negative-transfer avoidance. Neurocomputing 397: 309-319 (2020) - [j11]Firat Ismailoglu
Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes. IEEE ACM Trans. Comput. Biol. Bioinform. 17(1): 347-353 (2020) - 2019
- [j10]Nikolay Y. Nikolaev, Evgueni N. Smirnov, Daniel Stamate, Robert Zimmer:
A regime-switching recurrent neural network model applied to wind time series. Appl. Soft Comput. 80: 723-734 (2019) - 2017
- [j9]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers, Ralf Peeters
Conformal decision-tree approach to instance transfer. Ann. Math. Artif. Intell. 81(1-2): 85-104 (2017) - [j8]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers
Conformity-based source subset selection for instance transfer. Neurocomputing 258: 41-51 (2017) - [j7]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers
Testing exchangeability for transfer decision. Pattern Recognit. Lett. 88: 64-71 (2017) - 2015
- [j6]Shuang Zhou, Evgueni Nikolaevich Smirnov, Ralf Peeters
Conformal Region Classification with Instance-Transfer Boosting. Int. J. Artif. Intell. Tools 24(6): 1560002:1-1560002:25 (2015) - 2013
- [j5]Nikolay I. Nikolaev, Peter Tiño
Time-dependent series variance learning with recurrent mixture density networks. Neurocomputing 122: 501-512 (2013) - 2009
- [j4]Stijn Vanderlooy, Ida G. Sprinkhuizen-Kuyper, Evgueni N. Smirnov, H. Jaap van den Herik:
The ROC isometrics approach to construct reliable classifiers. Intell. Data Anal. 13(1): 3-37 (2009) - [j3]Evgueni N. Smirnov, Georgi I. Nalbantov, A. M. Kaptein:
Meta-conformity approach to reliable classification. Intell. Data Anal. 13(6): 901-915 (2009) - 2004
- [j2]Evgueni N. Smirnov, H. Jaap van den Herik, Ida G. Sprinkhuizen-Kuyper:
A Unifying Version-Space Representation. Ann. Math. Artif. Intell. 41(1): 47-76 (2004) - 1998
- [j1]Evgueni N. Smirnov:
A Report on the ECAI-98 Conference from the Machine Learning Perspective. AI Commun. 11(3-4): 233-235 (1998)
Conference and Workshop Papers
- 2023
- [c60]Evgueni N. Smirnov:
Coverage vs Acceptance-Error Curves for Conformal Classification Models. COPA 2023: 534-545 - [c59]Evgueni N. Smirnov, Richard Delava, Ron Diris, Nikolay I. Nikolaev:
Multi-view Semi-supervised Learning Using Privileged Information. EANN 2023: 144-152 - [c58]Carla Wrede
Towards Explainable Linguistic Summaries. FUZZ 2023: 1-6 - 2022
- [c57]Filip Schlembach
Conformal Multistep-Ahead Multivariate Time-Series Forecasting. COPA 2022: 316-318 - [c56]Husam Abdelqader, Evgueni N. Smirnov, Marc Pont
Conformal Decision Rules. COPA 2022: 319-321 - [c55]Husam Abdelqader, Evgueni N. Smirnov, Marc Pont, Marciano Geijselaers:
Interpretable and Reliable Rule Classification Based on Conformal Prediction. PKDD/ECML Workshops (1) 2022: 385-401 - 2021
- [c54]William Lopez Jaramillo, Evgueni N. Smirnov:
Shapley-value based inductive conformal prediction. COPA 2021: 52-71 - [c53]Christie Courtnage, Evgueni N. Smirnov:
Shapley-Value Data Valuation for Semi-supervised Learning. DS 2021: 94-108 - 2020
- [c52]Zachary Warnes, Magnus Kinder, Evgueni N. Smirnov:
Course Recommender Systems with Statistical Confidence. EDM 2020 - [c51]Raphaël Morsomme, Evgueni N. Smirnov:
Valid Prediction Intervals for Course Grades with Conformal Prediction. ICMLA 2020: 936-941 - 2019
- [c50]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers:
Ensembles based on conformal instance transfer. COPA 2019: 23-42 - [c49]Raphaël Morsomme, Evgueni N. Smirnov:
Conformal Prediction for Students' Grades in a Course Recommender System. COPA 2019: 196-213 - 2018
- [c48]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers, Ralf Peeters, Tao Jiang:
Conformal feature-selection wrappers for instance transfer. COPA 2018: 96-113 - [c47]Jelmer Neeven, Evgueni N. Smirnov:
Conformal stacked weather forecasting. COPA 2018: 220-233 - [c46]Dorian Beganovic, Evgueni N. Smirnov:
Ensemble Cross-Conformal Prediction. ICDM Workshops 2018: 870-877 - [c45]Florian Van Daalen
An Ensemble Approach to Time Dependent Classification. ICMLA 2018: 1007-1011 - [c44]Firat Ismailoglu, Evgueni N. Smirnov, Ralf Peeters, Shuang Zhou, Pieter Collins
Heterogeneous Domain Adaptation Based on Class Decomposition Schemes. PAKDD (1) 2018: 169-182 - 2017
- [c43]Fabian Braun, Olivier Caelen, Evgueni N. Smirnov, Steven Kelk, Bertrand Lebichot:
Improving Card Fraud Detection Through Suspicious Pattern Discovery. IEA/AIE (2) 2017: 181-190 - [c42]Kurt Driessens
UMAP 2017 EdRecSys Workshop Organizers' Welcome & Organization. UMAP (Adjunct Publication) 2017: 125-127 - [c41]Mara Houbraken, Chang Sun, Evgueni N. Smirnov, Kurt Driessens
Discovering Hidden Course Requirements and Student Competences from Grade Data. UMAP (Adjunct Publication) 2017: 147-152 - 2016
- [c40]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers
Decision Trees for Instance Transfer. COPA 2016: 116-127 - [c39]Nasser Davarzani, Ralf Peeters, Evgueni N. Smirnov, Joël M. H. Karel
Ranking Accuracy for Logistic-GEE Models. IDA 2016: 14-25 - 2015
- [c38]Shuang Zhou, Gijs Schoenmakers, Evgueni N. Smirnov, Ralf Peeters, Kurt Driessens, Siqi Chen:
Largest Source Subset Selection for Instance Transfer. ACML 2015: 423-438 - [c37]Firat Ismailoglu, Evgueni N. Smirnov, Ralf Peeters
Conformal ECOC Machines. ICTAI 2015: 361-368 - [c36]Shuang Zhou, Evgueni N. Smirnov, Gijs Schoenmakers, Ralf Peeters
A Non-parametric Conformity-Based Test for Transfer Decisions. ICTAI 2015: 628-635 - [c35]Firat Ismailoglu, Ida G. Sprinkhuizen-Kuyper, Evgueni N. Smirnov, Sergio Escalera
Fractional Programming Weighted Decoding for Error-Correcting Output Codes. MCS 2015: 38-50 - [c34]Firat Ismailoglu, Evgueni N. Smirnov, Nikolay Y. Nikolaev, Ralf Peeters
Instance-Based Decompositions of Error Correcting Output Codes. MCS 2015: 51-63 - 2014
- [c33]Nikolay Y. Nikolaev, Lilian M. de Menezes
Nonlinear filtering of asymmetric stochastic volatility models and Value-at-Risk estimation. CIFEr 2014: 310-317 - [c32]Elena Mocanu
Inexpensive user tracking using Boltzmann Machines. SMC 2014: 1-6 - 2013
- [c31]Evgueni N. Smirnov, Hua Zhang, Ralf Peeters, Nikolay I. Nikolaev, Maike Imkamp:
Aggregating Human-Expert Opinions for Multi-Label Classification. HCOMP (Works in Progress / Demos) 2013 - [c30]Shuang Zhou, Evgueni N. Smirnov, Haitham Bou-Ammar, Ralf Peeters:
Conformity-Based Transfer AdaBoost Algorithm. AIAI 2013: 401-410 - 2012
- [c29]Nikolay Y. Nikolaev, Evgueni N. Smirnov:
Analytical factor stochastic volatility modeling for portfolio allocation. CIFEr 2012: 1-8 - [c28]Alexandru Surpatean, Evgueni N. Smirnov, Nicolai Manie:
Master Orientation Tool. ECAI 2012: 995-996 - [c27]Alexandru Surpatean, Evgueni N. Smirnov, Nicolai Manie:
Similarity Functions for Collaborative Master Recommendations. EDM 2012: 230-231 - 2011
- [c26]Nikolay I. Nikolaev, Peter Tiño
Time-Dependent Series Variance Estimation via Recurrent Neural Networks. ICANN (1) 2011: 176-184 - [c25]Georgi I. Nalbantov, Andre Dekker
The Combination of Clinical, Dose-Related and Imaging Features Helps Predict Radiation-Induced Normal-Tissue Toxicity in Lung-cancer Patients - An in-silico Trial Using Machine Learning Techniques. ICMLA (2) 2011: 220-224 - 2010
- [c24]Evgueni N. Smirnov, Nikolay I. Nikolaev, Georgi I. Nalbantov:
Single-Stacking Conformity Approach to Reliable Classification. AIMSA 2010: 161-170 - [c23]Georgi I. Nalbantov, Evgueni N. Smirnov:
Soft Nearest Convex Hull Classifier. ECAI 2010: 841-846 - [c22]Nikolay Y. Nikolaev, Derrick Takeshi Mirikitani, Evgueni N. Smirnov:
Unscented grid filtering and elman recurrent networks. IJCNN 2010: 1-7 - [c21]Georgi I. Nalbantov, Evgueni N. Smirnov, Dilyan I. Nalbantov, Gerhard Weiss, Karl Nienhaus, Manuel Warcholik, Fiona Mavroudis:
Image Mining for Intelligent Autonomous Coal Mining. ICDM (Poster and Industry Proceedings) 2010: 17-23 - [c20]Evgueni N. Smirnov, Georgi I. Nalbantov, Nikolay I. Nikolaev:
k-Version-Space Multi-class Classification Based on k-Consistency Tests. ECML/PKDD (3) 2010: 277-292 - 2009
- [c19]Matthijs Moed, Evgueni N. Smirnov:
Efficient AdaBoost Region Classification. MLDM 2009: 123-136 - 2008
- [c18]J. van Prehn, Evgueni N. Smirnov:
Region Classification with Decision Trees. ICDM Workshops 2008: 53-59 - [c17]Evgueni N. Smirnov, Nikolay Y. Nikolaev, Georgi I. Nalbantov:
Description Identification and the Consistency Problem. SGAI Conf. 2008: 61-74 - 2007
- [c16]Nikolay Y. Nikolaev, Evgueni N. Smirnov:
A One-Step Unscented Particle Filter for Nonlinear Dynamical Systems. ICANN (1) 2007: 747-756 - 2006
- [c15]Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, Georgi I. Nalbantov, Stijn Vanderlooy:
Version Space Support Vector Machines. ECAI 2006: 809-810 - [c14]Evgueni N. Smirnov, Stijn Vanderlooy, Ida G. Sprinkhuizen-Kuyper:
Meta-Typicalness Approach to Reliable Classification. ECAI 2006: 811-812 - [c13]Evgueni N. Smirnov, A. M. Kaptein:
Theoretical and Experimental Study of a Meta-Typicalness Approach for Reliable Classification. ICDM Workshops 2006: 739-743 - [c12]Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, Nikolay I. Nikolaev:
Generalizing Version Space Support Vector Machines for Non-Separable Data. ICDM Workshops 2006: 744-748 - 2005
- [c11]Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, Georgi I. Nalbantov:
Reliable Instance Classification with Version Spaces. SGAI Conf. 2005: 288-301 - 2002
- [c10]Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, H. Jaap van den Herik:
Efficient Instance Retraction. AIMSA 2002: 21-30 - [c9]Evgueni N. Smirnov, H. Jaap van den Herik, Ida G. Sprinkhuizen-Kuyper:
Adaptable Boundary Sets. AI&M 2002 - [c8]Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, H. Jaap van den Herik:
New Version-Space Representations for Efficient Instance Retraction. KDID 2002: 32-49 - 2000
- [c7]Evgueni N. Smirnov, H. Jaap van den Herik:
Applying Preference Biases to Conjunctive and Disjunctive Version Spaces. AIMSA 2000: 321-330 - 1998
- [c6]Evgueni N. Smirnov, Peter J. Braspenning:
Version Space Retraction with Instance-Based Boundary Sets. AIMSA 1998: 389-402 - [c5]Evgueni N. Smirnov, Peter J. Braspenning:
Version Space Learning with Instance-Based Boundary Sets. ECAI 1998: 460-464 - 1996
- [c4]Nikolay I. Nikolaev, Evgueni N. Smirnov:
Stochastically Guided Disjunctive Version Space Learning. ECAI 1996: 443-447 - 1995
- [c3]Nikolay I. Nikolaev, Evgueni N. Smirnov:
Analytical Learning Guided by Empirical Technology: An Approach to Integration (Extended Abstract). ECML 1995: 327-330 - [c2]Evgueni N. Smirnov, Nikolay I. Nikolaev:
Multiple Explanation-based Learning Guided by Space Fragmenting. SCAI 1995: 85-96 - 1992
- [c1]Evgueni N. Smirnov:
Space Fragmenting - A Method of Disjunctive Concept Acquisition. AIMSA 1992: 97-104
Parts in Books or Collections
- 2011
- [p2]Evgueni N. Smirnov, Matthijs Moed, Georgi I. Nalbantov, Ida G. Sprinkhuizen-Kuyper
Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification. Ensembles in Machine Learning Applications 2011: 39-58 - 2004
- [p1]Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, H. Jaap van den Herik:
One-Sided Instance-Based Boundary Sets. Database Support for Data Mining Applications 2004: 270-288
Editorship
- 2020
- [e3]Alexander Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni N. Smirnov, Giovanni Cherubin, Marco Christini:
Conformal and Probabilistic Prediction and Applications, COPA 2020, 9-11 September 2020, Virtual Event. Proceedings of Machine Learning Research 128, PMLR 2020 [contents] - 2019
- [e2]Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni N. Smirnov:
Conformal and Probabilistic Prediction and Applications, COPA 2019, 9-11 September 2019, Golden Sands, Bulgaria. Proceedings of Machine Learning Research 105, PMLR 2019 [contents] - 2018
- [e1]Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni N. Smirnov, Ralf L. M. Peeters:
7th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2018, 11-13 June 2018, Maastricht, The Netherlands. Proceedings of Machine Learning Research 91, PMLR 2018 [contents]
Coauthor Index
aka: Nikolay Y. Nikolaev
aka: Ralf Peeters
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