Vladimir Vapnik
E367287
Vladimir Vapnik is a pioneering computer scientist and statistician best known as a co-inventor of support vector machines and a founder of statistical learning theory.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Vladimir Vapnik canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T3542918 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vladimir Vapnik Context triple: [Léon Bottou, hasAcademicAdvisor, Vladimir Vapnik]
-
A.
Léon Bottou
Léon Bottou is a French computer scientist known for his influential work in machine learning and neural networks, including key contributions to the development of the LeNet convolutional network.
-
B.
Gregory Piatetsky-Shapiro
Gregory Piatetsky-Shapiro is a pioneering computer scientist and data mining expert best known as the founder of the KDD (Knowledge Discovery and Data Mining) conferences and the KDnuggets data science community.
-
C.
Emanuel Parzen
Emanuel Parzen was an American statistician renowned for pioneering kernel density estimation, particularly through the development of the Parzen window method.
-
D.
Michael P. Kearns
Michael P. Kearns is an American politician from New York who has served in various local and state offices, including roles in the New York State Assembly and Erie County government.
-
E.
Ruslan Salakhutdinov
Ruslan Salakhutdinov is a prominent machine learning researcher known for his contributions to deep learning and probabilistic graphical models, and for serving as Director of AI Research at Apple and a professor at Carnegie Mellon University.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vladimir Vapnik Target entity description: Vladimir Vapnik is a pioneering computer scientist and statistician best known as a co-inventor of support vector machines and a founder of statistical learning theory.
-
A.
Léon Bottou
Léon Bottou is a French computer scientist known for his influential work in machine learning and neural networks, including key contributions to the development of the LeNet convolutional network.
-
B.
Gregory Piatetsky-Shapiro
Gregory Piatetsky-Shapiro is a pioneering computer scientist and data mining expert best known as the founder of the KDD (Knowledge Discovery and Data Mining) conferences and the KDnuggets data science community.
-
C.
Emanuel Parzen
Emanuel Parzen was an American statistician renowned for pioneering kernel density estimation, particularly through the development of the Parzen window method.
-
D.
Michael P. Kearns
Michael P. Kearns is an American politician from New York who has served in various local and state offices, including roles in the New York State Assembly and Erie County government.
-
E.
Ruslan Salakhutdinov
Ruslan Salakhutdinov is a prominent machine learning researcher known for his contributions to deep learning and probabilistic graphical models, and for serving as Director of AI Research at Apple and a professor at Carnegie Mellon University.
- F. None of above. chosen
Statements (58)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
machine learning researcher ⓘ mathematician ⓘ person ⓘ statistician ⓘ |
| authorOf |
Estimation of Dependences Based on Empirical Data
ⓘ
Statistical Learning Theory ⓘ
linked to:
The Nature of Statistical Learning Theory
The Nature of Statistical Learning Theory ⓘ |
| awardReceived |
ACM SIGKDD Innovation Award
ⓘ
linked to:
SIGKDD Innovation Award
BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies ⓘ
linked to:
BBVA Foundation Frontiers of Knowledge Award
Gabor Award of the International Neural Network Society ⓘ IEEE Neural Networks Pioneer Award ⓘ |
| citizenship |
Russia
ⓘ
United States of America ⓘ |
| coDeveloped |
VC dimension concept
ⓘ
Vapnik–Chervonenkis theory ⓘ structural risk minimization principle ⓘ |
| coInvented |
support vector machines
ⓘ
linked to:
Support Vector Machines
|
| educatedAt | Moscow State University ⓘ |
| employer |
AT&T Bell Labs
ⓘ
linked to:
Bell Telephone Laboratories
Columbia University ⓘ Facebook AI Research ⓘ
linked to:
Meta AI
NEC Research Institute ⓘ Royal Holloway, University of London ⓘ |
| fieldOfWork |
machine learning
ⓘ
optimization ⓘ pattern recognition ⓘ statistical learning theory ⓘ statistics ⓘ |
| hasCollaborator |
Alexey Chervonenkis
ⓘ
Corinna Cortes ⓘ Isabelle Guyon ⓘ Léon Bottou ⓘ |
| hasConceptNamedAfter |
VC dimension
ⓘ
Vapnik–Chervonenkis classes ⓘ
linked to:
Vapnik–Chervonenkis theory
Vapnik–Chervonenkis theory ⓘ |
| influenced |
modern machine learning theory
ⓘ
support vector machine research community ⓘ |
| influencedBy |
Andrey Kolmogorov
ⓘ
linked to:
Andrei Kolmogorov
Russian school of probability and statistics ⓘ
linked to:
Soviet school of probability theory
|
| knownFor |
VC dimension
ⓘ
Vapnik–Chervonenkis theory ⓘ
linked to:
Computational Learning Theory
maximum margin classifier ⓘ statistical learning theory ⓘ structural risk minimization ⓘ support vector machines ⓘ
linked to:
Support Vector Machines
theory of generalization in machine learning ⓘ |
| notableIdea |
capacity control via VC dimension
ⓘ
maximum margin hyperplane in classification ⓘ structural risk minimization principle ⓘ |
| notableWork |
Estimation of Dependences Based on Empirical Data
ⓘ
Statistical Learning Theory ⓘ
linked to:
The Nature of Statistical Learning Theory
The Nature of Statistical Learning Theory ⓘ |
| positionHeld |
professor at Columbia University
ⓘ
professor at Royal Holloway, University of London ⓘ research scientist at AT&T Bell Labs ⓘ research scientist at Facebook AI Research ⓘ research scientist at NEC Research Institute ⓘ
linked to:
NEC Research Institute
|
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Vladimir Vapnik Description of subject: Vladimir Vapnik is a pioneering computer scientist and statistician best known as a co-inventor of support vector machines and a founder of statistical learning theory.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.