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2020 – today
- 2021
[c24]Alexey Morozov, Brian Angulo, Vadim Mottl, Alexander Tatarchuk, Olga Krasotkina:
Differential Leave-One-Out Cross-Validation for Feature Selection in Generalized Linear Dependence Models. ITCC 2021: 47-56
2010 – 2019
- 2019
- [c23]Valentina Sulimova, Sergey Bukhonov, Olga Krasotkina, Vadim Mottl, Annette Sterr, Kevin Wells, David Windridge:
Sparse Multimodal Classification of EEG Signals from Rapid Serial Visual Presentation of Diagnostic Images. MLDM (1) 2019: 355-366 - [c22]Vadim Mottl, Olga Krasotkina, Valentina Sulimova, Alexey Morozov, Ilya Pugach, Alexander Tatarchuk:
Linear complexity algorithms for high dimensional SVM and regression problems with smart sparse regularization. MLDM (1) 2019: 412-430 - 2018
- [c21]Valentina Sulimova, Alexander Zhukov, Olga Krasotkina, Vadim Mottl, Anatoly Markov
:
Automatic Rail Flaw Localization and Recognition by Featureless Ultrasound Signal Analysis. MLDM (1) 2018: 16-27 - [c20]Olga Krasotkina, Michael Markov, Vadim Mottl, Ilya Pugach, Dmitry Babichev, Alexey Morozov:
Constrained Regularized Regression Model Search in Large Sets of Regressors. MLDM (2) 2018: 394-408 - 2017
- [c19]Vadim Mottl, Oleg Seredin, Olga Krasotkina:
Compactness Hypothesis, Potential Functions, and Rectifying Linear Space in Machine Learning. Braverman Readings in Machine Learning 2017: 52-102 - [c18]Olga Krasotkina, Vadim Mottl, Michael Markov, Elena Chernousova, Dmitry Malakhov:
Methods of Hyperparameter Estimation in Time-Varying Regression Models with Application to Dynamic Style Analysis of Investment Portfolios. MLDM 2017: 431-450 - 2016
- [c17]Anton Malenichev, Olga Krasotkina, Vadim Mottl, Oleg Seredin:
Multi-class Classification in Big Data. AIST (Supplement) 2016: 203-211 - [c16]Pavel A. Turkov, Olga Krasotkina, Vadim Mottl, Alexey Sychugov:
Feature Selection for Handling Concept Drift in the Data Stream Classification. MLDM 2016: 614-629 - [c15]Zhosan Yulia, Olga Krasotkina, Vadim Mottl:
Sparse logistic regression with supervised selectivity for predictors selection in credit scoring. SoICT 2016: 167-172 - 2015
- [c14]Inessa Gracheva, Andrey Kopylov, Olga Krasotkina:
Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model. AIST 2015: 71-82 - [c13]Olga Krasotkina, Oleg Seredin, Vadim Mottl:
Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation. MCS 2015: 89-99 - [c12]Olga Krasotkina, Vadim Mottl:
A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization. MLDM 2015: 382-394 - [c11]Olga Krasotkina, Vadim Mottl:
A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis. MLDM 2015: 425-437 - 2014
- [c10]Anton Malenichev, Valentina Sulimova, Olga Krasotkina, Vadim Mottl, Anatoly Markov:
An Automatic Matching Procedure of Ultrasonic Railway Defectograms. MLDM 2014: 315-327 - 2013
- [c9]Pavel A. Turkov, Olga Krasotkina, Vadim Mottl:
Dynamic Programming for Bayesian Logistic Regression Learning under Concept Drift. PReMI 2013: 190-195 - [c8]Anton Malenichev, Olga Krasotkina:
Real-Time Smoke Detection in Video Sequences: Combined Approach. PReMI 2013: 445-450 - 2012
- [c7]Pavel A. Turkov, Olga Krasotkina, Vadim Mottl:
The Bayesian logistic regression in pattern recognition problems under concept drift. ICPR 2012: 2976-2979 - [c6]Pavel A. Turkov, Olga Krasotkina, Vadim Mottl:
Bayesian Approach to the Concept Drift in the Pattern Recognition Problems. MLDM 2012: 1-10 - 2011
- [c5]Olga Krasotkina, Vadim Mottl, Pavel A. Turkov:
Bayesian Approach to the Pattern Recognition Problem in Nonstationary Environment. PReMI 2011: 24-29 - 2010
- [c4]Andrey Kopylov, Olga Krasotkina, Oleksandr Pryimak, Vadim Mottl:
A Signal Processing Algorithm Based on Parametric Dynamic Programming. ICISP 2010: 280-286
2000 – 2009
- 2008
- [c3]Olga Krasotkina, Vadim Mottl:
Adaptive nonstationary regression analysis. ICPR 2008: 1-4 - 2007
- [c2]Vadim Mottl, Alexander Tatarchuk, Valentina Sulimova, Olga Krasotkina, Oleg Seredin:
Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion. MCS 2007: 1-12 - 2005
- [c1]Vadim Mottl, Olga Krasotkina, Oleg Seredin, Ilya B. Muchnik:
Principles of Multi-kernel Data Mining. MLDM 2005: 52-61
Coauthor Index
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