Vladimir Vapnik
#966
Most Influential Person Now
Russian mathematician
Vladimir Vapnik's AcademicInfluence.com Rankings
Vladimir Vapnikcomputer-science Degrees
Computer Science
#57
World Rank
#59
Historical Rank
#33
USA Rank
Machine Learning
#5
World Rank
#5
Historical Rank
#2
USA Rank
Vladimir Vapnikmathematics Degrees
Mathematics
#474
World Rank
#930
Historical Rank
#212
USA Rank
Measure Theory
#71
World Rank
#118
Historical Rank
#39
USA Rank
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Computer Science Mathematics
Why Is Vladimir Vapnik Influential?
(Suggest an Edit or Addition)According to Wikipedia, Vladimir Naumovich Vapnik is a computer scientist, researcher, and academic. He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning and the co-inventor of the support-vector machine method and support-vector clustering algorithms.
Vladimir Vapnik's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- The Nature of Statistical Learning Theory (2000) (40661)
- Support-Vector Networks (1995) (36586)
- Statistical learning theory (1998) (27140)
- A training algorithm for optimal margin classifiers (1992) (11660)
- An overview of statistical learning theory (1999) (5298)
- Support-vector networks (2004) (5236)
- The Nature of Statistical Learning (1995) (4898)
- Gene Selection for Cancer Classification using Support Vector Machines (2002) (4754)
- Support Vector Regression Machines (1996) (4145)
- Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities (1971) (3921)
- Support Vector Method for Function Approximation, Regression Estimation and Signal Processing (1996) (2813)
- Estimation of Dependences Based on Empirical Data (2006) (2349)
- Choosing Multiple Parameters for Support Vector Machines (2002) (2336)
- Support vector machines for histogram-based image classification (1999) (1525)
- Support Vector Clustering (2002) (1480)
- Support vector machines for spam categorization (1999) (1479)
- Comparing support vector machines with Gaussian kernels to radial basis function classifiers (1997) (1382)
- Pattern recognition using generalized portrait method (1963) (1216)
- Feature Selection for SVMs (2000) (1167)
- Predicting Time Series with Support Vector Machines (1997) (1023)
- Principles of Risk Minimization for Learning Theory (1991) (746)
- Comparison of learning algorithms for handwritten digit recognition (1995) (668)
- Extracting Support Data for a Given Task (1995) (667)
- A new learning paradigm: Learning using privileged information (2009) (654)
- Comparison of classifier methods: a case study in handwritten digit recognition (1994) (645)
- Local Learning Algorithms (1992) (603)
- The Support Vector Method of Function Estimation (1998) (550)
- Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics) (1982) (495)
- Learning algorithms for classification: A comparison on handwritten digit recognition (1995) (492)
- Parallel Support Vector Machines: The Cascade SVM (2004) (484)
- Learning by Transduction (1998) (431)
- Model Selection for Support Vector Machines (1999) (408)
- The Nature of Statistical Learning Theory, Second Edition (2000) (404)
- Boosting and Other Ensemble Methods (1994) (375)
- Measuring the VC-Dimension of a Learning Machine (1994) (374)
- Unifying distillation and privileged information (2015) (371)
- Prior Knowledge in Support Vector Kernels (1997) (360)
- Incorporating Invariances in Support Vector Learning Machines (1996) (331)
- Neural Information Processing Systems (1997) (326)
- Learning using privileged information: similarity control and knowledge transfer (2015) (323)
- Using support vector machines for time series prediction (1999) (272)
- Vicinal Risk Minimization (2000) (256)
- Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models (1996) (256)
- Discovering Informative Patterns and Data Cleaning (1996) (235)
- Automatic Capacity Tuning of Very Large VC-Dimension Classifiers (1992) (235)
- Necessary and Sufficient Conditions for the Uniform Convergence of Means to their Expectations (1982) (215)
- A note one class of perceptrons (1964) (212)
- Support Vector Method for Multivariate Density Estimation (1999) (200)
- Inference with the Universum (2006) (189)
- Model complexity control for regression using VC generalization bounds (1999) (187)
- Kernel Dependency Estimation (2002) (185)
- What Size Test Set Gives Good Error Rate Estimates? (1998) (167)
- Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression (2011) (160)
- Support vector regression with ANOVA decomposition kernels (1999) (144)
- Support vector density estimation (1999) (140)
- Learning Curves: Asymptotic Values and Rate of Convergence (1993) (139)
- Structural Risk Minimization for Character Recognition (1991) (127)
- A support vector clustering method (2000) (125)
- Model Selection for Small Sample Regression (2002) (116)
- Local Algorithms for Pattern Recognition and Dependencies Estimation (1993) (115)
- The Support Vector Method (1997) (100)
- Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics) (2006) (97)
- Knowledge transfer in SVM and neural networks (2017) (89)
- Transductive Inference for Estimating Values of Functions (1999) (78)
- Universal learning technology : Support vector machines (2005) (69)
- A Support Vector Method for Clustering (2000) (68)
- Methods of Pattern Recognition (2000) (67)
- Estimation of dependences based on empirical data ; : Empirical inference science : afterword of 2006 (2006) (63)
- On the theory of learning with Privileged Information (2010) (59)
- Inductive principles of the search for empirical dependences (methods based on weak convergence of probability measures) (1989) (59)
- SMO-Style Algorithms for Learning Using Privileged Information (2010) (55)
- Introduction: Four Periods in the Research of the Learning Problem (1995) (54)
- Writer-adaptation for on-line handwritten character recognition (1993) (48)
- Three remarks on the support vector method of function estimation (1999) (47)
- Learning using hidden information (Learning with teacher) (2009) (45)
- Rethinking statistical learning theory: learning using statistical invariants (2018) (44)
- A Case Study in Handwritten Digit Recognition (1994) (42)
- On the Theory of Learnining with Privileged Information (2010) (40)
- Transductive Inference and Semi-Supervised Learning (2006) (40)
- Boosting and Other Machine Learning Algorithms (1994) (39)
- Computer aided cleaning of large databases for character recognition (1992) (38)
- Density Estimation using Support Vector Machines (1998) (37)
- Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions (2009) (34)
- Bounds on error expectation for SVM (2000) (31)
- Learning using hidden information: Master-class learning (2007) (30)
- Constructing Learning Algorithms (1995) (29)
- On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities (2013) (27)
- SVM method of estimating density, conditional probability, and conditional density (2000) (27)
- Learning with Rigorous Support Vector Machines (2003) (27)
- Capacity control in linear classifiers for pattern recognition (1992) (26)
- Direct Methods in Statistical Learning Theory (2000) (25)
- Large margin vs. large volume in transductive learning (2008) (23)
- Multivariate Density Estimation: an SVM Approach (1999) (22)
- Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis (2015) (21)
- Reinforced SVM method and memorization mechanisms (2021) (20)
- Multidimensional splines with infinite number of knots as SVM kernels (2013) (18)
- The Vicinal Risk Minimization Principle and the SVMs (2000) (17)
- Complete Statistical Theory of Learning (2019) (16)
- Learning with Intelligent Teacher (2016) (13)
- Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis (2015) (13)
- Synergy of Monotonic Rules (2016) (13)
- Three fundamental concepts of the capacity of learning machines (1993) (13)
- V-matrix method of solving statistical inference problems (2015) (12)
- Methods of Function Estimation (2000) (11)
- Constructive Setting of the Density Ratio Estimation Problem and its Rigorous Solution (2013) (9)
- Complete statistical theory of learning: learning using statistical invariants (2020) (9)
- Learning hidden information: SVM+ (2006) (9)
- Support Vector Machine for Text Categorization (1998) (9)
- Controlling the Generalization Ability of Learning Processes (1995) (9)
- Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates (1995) (9)
- A Constructive Setting for the Problem of Density Ratio Estimation (2014) (8)
- Bounds on the Rate of Convergence of Learning Processes (1995) (6)
- Multivariate Density Estimation: a Support Vector Machine Approach (1999) (6)
- Constructive setting for problems of density ratio estimation (2015) (5)
- Conclusion: What Is Important in Learning Theory? (1995) (4)
- PROBABILISTIC PROGRAMMING FOR ADVANCED MACHINE LEARNING (PPAML) DISCRIMINATIVE LEARNING FOR GENERATIVE TASKS (DILIGENT) (2017) (3)
- Learning and generalization: theoretical bounds (1998) (3)
- Consistency of Learning Processes (1995) (3)
- Estimation of dependencies based on small number of observations (1995) (2)
- Setting of the Learning Problem (1995) (1)
- A Method of Minimizing Empirical Risk for the Problem of Pattern Recognition (2006) (1)
- Statistical Theory of Generalization (Abstract) (1996) (1)
- Realism and Instrumentalism: Classical Statistics and VC Theory (1960–1980) (2006) (1)
- Numerical simulation of laser damage to an optical material with defects (1987) (0)
- Empirical inference problems (2003) (0)
- [Isolation of a group at risk for stomach cancer based on genetic and epidemiological study data]. (1983) (0)
- COMPARISONOFLEARNING ALGORITHMS FORHANDWRITTEN DIGIT RECOGNITION (1995) (0)
- Machine Learning (2021) (0)
- Knowledge transfer in SVM and neural networks (2017) (0)
- [Use of mathematical methods and computers for diagnosis of primary rheumocarditis in children]. (1975) (0)
- Support Vector Learning Support Vector Learning Contents Summary 11 1 Introduction and Preliminaries 15 2 Support Vector Machines 33 4 Prior Knowledge in Support Vector Machines 99 5 Conclusion 125 a Object Databases 127 B Object Recognition Results 137 (1997) (0)
- Predicting transportpath degradation/failure based on recent performance history (1994) (0)
- On the Eeective Vc Dimension (1994) (0)
- Falsifiability and Parsimony: VC Dimension and the Number of Entities (1980–2000) (2006) (0)
- [Use of a computer in the differential diagnosis of the exophytic form of cancer and benign tumors of the esophagus]. (1977) (0)
- Noninductive Methods of Inference: Direct Inference Instead of Generalization (2000–…) (2006) (0)
- Methods of Parametric Statistics for the Problem of Regression Estimation (2006) (0)
- Methods of Expected-Risk Minimization (2006) (0)
- Model Sele tion for Small Sample (2000) (0)
- Svms for Histogram-based Image Classiication List of Figures (1999) (0)
- Lecture 16 (2018) (0)
- Marathi Handwritten Numeral Recognition using Zernike Moments and Fourier Descriptors (2020) (0)
- Methods of Parametric Statistics for the Pattern Recognition Problem (2006) (0)
- Solution of Ill-posed Problems. Interpretation of Measurements Using the Method of Structural Risk Minimization (2006) (0)
- Learning Methods in Problems of Diagnosis (1968) (0)
- Rethinking statistical learning theory: learning using statistical invariants (2018) (0)
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What Schools Are Affiliated With Vladimir Vapnik?
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