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ORCIDAlexander P. Kuleshov
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2020 – today
- 2021
[j4]Andrzej Cichocki
, Alexander P. Kuleshov:
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles. Comput. Intell. Neurosci. 2021: 8893795:1-8893795:21 (2021)
2010 – 2019
- 2018
- [c19]Alexander P. Kuleshov, Alexander Bernstein, Evgeny Burnaev:
Manifold Learning Regression with Non-stationary Kernels. ANNPR 2018: 152-164 - [c18]Alexander P. Kuleshov, Alexander Bernstein, Evgeny Burnaev:
Conformal prediction in manifold learning. COPA 2018: 234-253 - [c17]Alexander P. Kuleshov, Alexander Bernstein, Evgeny Burnaev:
Kernel Regression on Manifold Valued Data. DSAA 2018: 120-129 - 2017
- [j3]Alexander P. Kuleshov, Alexander Bernstein:
Nonlinear multi-output regression on unknown input manifold. Ann. Math. Artif. Intell. 81(1-2): 209-240 (2017) - [c16]Alexander P. Kuleshov, Alexander Bernstein, Yury Yanovich:
High-Dimensional Density Estimation for Data Mining Tasks. ICDM Workshops 2017: 523-530 - [c15]Alexander P. Kuleshov, Alexander Bernstein, Evgeny Burnaev, Yury Yanovich:
Machine Learning in Appearance-Based Robot Self-Localization. ICMLA 2017: 106-112 - [c14]Alexander P. Kuleshov, Alexander Bernstein, Evgeny Burnaev:
Mobile Robot Localization via Machine Learning. MLDM 2017: 276-290 - [i2]Alexander P. Kuleshov, Alexander Bernstein, Evgeny Burnaev:
Mobile Robot Localization via Machine Learning. CoRR abs/1707.03469 (2017) - 2016
- [c13]Alexander P. Kuleshov, Alexander V. Bernstein:
Incremental Construction of Low-Dimensional Data Representations. ANNPR 2016: 55-67 - [c12]Alexander P. Kuleshov, Alexander V. Bernstein:
Extended Regression on Manifolds Estimation. COPA 2016: 208-228 - [c11]Alexander P. Kuleshov, Alexander V. Bernstein:
Regression on High-Dimensional Inputs. ICDM Workshops 2016: 732-739 - [c10]Alexander P. Kuleshov, Alexander V. Bernstein:
Statistical Learning on Manifold-Valued Data. MLDM 2016: 311-325 - 2015
- [c9]Alexander V. Bernstein, Alexander P. Kuleshov, Yury Yanovich:
Information preserving and locally isometric&conformal embedding via Tangent Manifold Learning. DSAA 2015: 1-9 - [c8]Alexander V. Bernstein, Alexander P. Kuleshov, Yury Yanovich:
Statistical Learning via Manifold Learning. ICMLA 2015: 64-69 - [c7]Alexander P. Kuleshov, Alexander V. Bernstein, Yury Agalakov:
Data-Based Statistical Models of Data Networks. ICMLA 2015: 433-438 - [c6]Alexander V. Bernstein, Alexander P. Kuleshov, Yury Yanovich:
Locally isometric and conformal parameterization of image manifold. ICMV 2015: 987507 - [c5]Alexander V. Bernstein, Alexander P. Kuleshov, Yury Yanovich:
Manifold Learning in Regression Tasks. SLDS 2015: 414-423 - 2014
- [c4]Alexander V. Bernstein, Alexander P. Kuleshov:
Low-Dimensional Data Representation in Data Analysis. ANNPR 2014: 47-58 - [c3]Alexander V. Bernstein, Alexander P. Kuleshov:
Dimensionality Reduction in Statistical Learning. ICMLA 2014: 330-335 - [c2]Alexander P. Kuleshov, Alexander V. Bernstein:
Manifold Learning in Data Mining Tasks. MLDM 2014: 119-133 - 2013
- [j2]Alexander V. Bernstein, Alexander P. Kuleshov:
Manifold Learning: Generalization Ability and Tangent Proximity. Int. J. Softw. Informatics 7(3): 359-390 (2013) - [c1]Alexander P. Kuleshov, Alexander V. Bernstein:
Tangent bundle Manifold Learning for image analysis. ICMV 2013: 906720 - 2012
- [i1]Alexander V. Bernstein, Alexander P. Kuleshov:
Tangent Bundle Manifold Learning via Grassmann&Stiefel Eigenmaps. CoRR abs/1212.6031 (2012)
2000 – 2009
- 2008
- [j1]Alexander V. Bernstein, Alexander P. Kuleshov:
Optimal filtering of a random background in image processing problems. Probl. Inf. Transm. 44(3): 233-242 (2008)
Coauthor Index
aka: Alexander Bernstein
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