BibSLEIGH — Vadim

Travelled to:

1 × Australia
1 × Austria
1 × Japan
1 × Russia
1 × Spain
1 × Sweden
1 × USA
1 × United Kingdom
3 × Canada
5 × Germany

Collaborated with:

O.Krasotkina A.Kopylov O.Seredin I.B.Muchnik A.Kostin S.Dvoenko P.A.Turkov V.Sulimova J.Kittler A.Yermakov A.Blinov A.Tatarchuk D.Windridge C.A.Kulikowski M.Lange V.Levyant E.Chernousova P.Levdik A.Gubareva A.Larin A.Malenichev A.Markov N.Razin

Talks about:

recognit (8) pattern (7) bayesian (4) problem (4) imag (4) data (4) approach (3) select (3) kernel (3) space (3)

Person: Vadim Mottl

DBLP: Mottl:Vadim

Contributed to:

2015

2014

2012

2009

2008

2005

2004

2002

2001

2000

1998

1996

Wrote 20 papers:

MLDM-2015-KrasotkinaM #approach #optimisation #ranking: A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.
MLDM-2015-KrasotkinaM15a #analysis #approach: A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis (OK, VM), pp. 425–437.
ICPR-2014-ChernousovaLTMW #parametricity #validation: Non-enumerative Cross Validation for the Determination of Structural Parameters in Feature-Selective SVMs (EC, PL, AT, VM, DW), pp. 3654–3659.
ICPR-2014-GubarevaSSLM #linear: Finding the Largest Hypercavity in a Linear Data Space (AG, VS, OS, AL, VM), pp. 4406–4410.
MLDM-2014-MalenichevSKMM #automation: An Automatic Matching Procedure of Ultrasonic Railway Defectograms (AM, VS, OK, VM, AM), pp. 315–327.
ICPR-2012-SeredinMTRW #multimodal #pattern matching #pattern recognition #recognition: Convex support and Relevance Vector Machines for selective multimodal pattern recognition (OS, VM, AT, NR, DW), pp. 1647–1650.
ICPR-2012-TurkovKM #concept #pattern matching #pattern recognition #problem #recognition: The Bayesian logistic regression in pattern recognition problems under concept drift (PAT, OK, VM), pp. 2976–2979.
MLDM-2012-TurkovKM #approach #concept #pattern matching #pattern recognition #problem #recognition: Bayesian Approach to the Concept Drift in the Pattern Recognition Problems (PAT, OK, VM), pp. 1–10.
MLDM-2009-SeredinKM #machine learning #order #set: Selection of Subsets of Ordered Features in Machine Learning (OS, AK, VM), pp. 16–28.
ICPR-2008-MottlLSY #kernel #online #verification: Signature verification based on fusion of on-line and off-line kernels (VM, ML, VS, AY), pp. 1–4.
MLDM-2005-MottlKSM #data mining #kernel #mining #multi: Principles of Multi-kernel Data Mining (VM, OK, OS, IBM), pp. 52–61.
ICPR-v1-2004-MottlDK #algorithm #pattern matching #pattern recognition #problem #recognition: Pattern Recognition in Interrelated Data: The Problem, Fundamental Assumptions, Recognition Algorithms (VM, SD, AK), pp. 188–191.
ICPR-v2-2002-MottlSDKM #pattern matching #pattern recognition #recognition: Featureless Pattern Recognition in an Imaginary Hilbert Space (VM, OS, SD, CAK, IBM), pp. 88–91.
ICPR-v3-2002-MottlKKYK #grid #identification #image #similarity: Elastic Transformation of the Image Pixel Grid for Similarity Based Face Identification (VM, AK, AK, AY, JK), pp. 549–552.
ICPR-v4-2002-MottlKK #classification #identification #kernel: Support Object Classifiers with Rigid and Elastic Kernel Functions for Face Identification (VM, AK, JK), pp. 205–208.
MLDM-2001-MottlDSKM #classification #pattern matching #pattern recognition #recognition: Featureless Pattern Recognition in an Imaginary Hilbert Space and Its Application to Protein Fold Classification (VM, SD, OS, CAK, IBM), pp. 322–336.
ICPR-v2-2000-MottlDLM #pattern matching #pattern recognition #recognition: Pattern Recognition in Spatial Data: A New Method of Seismic Explorations for Oil and Gas in Crystalline Basement Rocks (VM, SD, VL, IBM), pp. 2315–2318.
ICPR-1998-MottlBKKM #analysis #image: Variational methods in signal and image analysis (VM, AB, AK, AK, IBM), pp. 525–527.
ICPR-1998-MottlKK #image: Edge-preserving in generalized smoothing of signals and images (VM, AK, AK), pp. 1579–1581.
ICPR-1996-MottlMBK #image #problem: Hidden tree-like quasi-Markov model and generalized technique for a class of image processing problems (VM, IBM, AB, AK), pp. 715–719.