Support Vector Machines

E426671

binary classifier classification algorithm kernel method large-margin method margin-based classifier regression algorithm supervised learning algorithm

Support Vector Machines are a class of supervised learning algorithms used primarily for classification and regression tasks, which work by finding the optimal separating hyperplane between data classes in a high-dimensional feature space.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (5)

Label	Occurrences
support vector machines	2
C-Support Vector Classification	1
Support Vector Machines canonical	1
Support-Vector Networks	1
Support-Vector Networks (1995)	1

How this entity was disambiguated

This entity first appeared as the object of triple T4277216 — 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: Support Vector Machines
Context triple: [SVC, basedOn, Support Vector Machines]

A. Probably Approximately Correct learning (PAC learning)
Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
B. Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
C. Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
D. “Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
E. Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
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: Support Vector Machines
Target entity description: Support Vector Machines are a class of supervised learning algorithms used primarily for classification and regression tasks, which work by finding the optimal separating hyperplane between data classes in a high-dimensional feature space.

A. Probably Approximately Correct learning (PAC learning)
Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
B. Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
C. Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
D. “Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
E. Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
F. None of above. chosen

Statements (63)

Predicate	Object
instanceOf	binary classifier ⓘ classification algorithm ⓘ kernel method ⓘ large-margin method ⓘ margin-based classifier ⓘ regression algorithm ⓘ supervised learning algorithm ⓘ
advantage	effective in high-dimensional spaces ⓘ robust to overfitting with appropriate regularization ⓘ
basedOn	statistical learning theory ⓘ structural risk minimization ⓘ
commonKernel	linear kernel ⓘ polynomial kernel ⓘ radial basis function kernel ⓘ sigmoid kernel ⓘ
developedBy	Alexey Chervonenkis NERFINISHED ⓘ Vladimir Vapnik NERFINISHED ⓘ
disadvantage	choice of kernel and parameters can be difficult ⓘ training can be slow on very large datasets ⓘ
goal	find optimal separating hyperplane ⓘ maximize margin between classes ⓘ
handles	binary classification ⓘ high-dimensional data ⓘ linearly separable data ⓘ multiclass classification via reduction strategies ⓘ nonlinearly separable data ⓘ regression via Support Vector Regression ⓘ
hasHyperparameter	C regularization parameter ⓘ degree for polynomial kernel ⓘ epsilon for SVR ⓘ gamma for RBF kernel ⓘ kernel parameters ⓘ kernel type ⓘ
hasVariant	C-SVM NERFINISHED ⓘ Support Vector Regression NERFINISHED ⓘ hard-margin SVM NERFINISHED ⓘ one-class SVM NERFINISHED ⓘ soft-margin SVM NERFINISHED ⓘ ν-SVM ⓘ
implementedIn	LIBLINEAR NERFINISHED ⓘ LIBSVM NERFINISHED ⓘ R e1071 package NERFINISHED ⓘ scikit-learn NERFINISHED ⓘ
introducedIn	1990s ⓘ
optimizationProblem	convex optimization problem ⓘ quadratic programming problem ⓘ
property	global optimum guaranteed due to convexity ⓘ sparse solution in terms of support vectors ⓘ
relatedTo	kernel ridge regression ⓘ logistic regression ⓘ maximum margin classifier ⓘ perceptron ⓘ
usedFor	bioinformatics classification tasks ⓘ handwritten digit recognition ⓘ image classification ⓘ text classification ⓘ
usesConcept	Lagrange multipliers NERFINISHED ⓘ feature space ⓘ hyperplane ⓘ kernel function ⓘ margin ⓘ quadratic programming ⓘ support vector ⓘ

How these facts were elicited

Referenced by (6)

Full triples — surface form annotated when it differs from this entity's canonical label.

SVC → basedOn → Support Vector Machines ⓘ

this entity surface form: C-Support Vector Classification

Corinna Cortes → coAuthorOf → Support Vector Machines ⓘ

this entity surface form: Support-Vector Networks

Corinna Cortes → notableWork → Support Vector Machines ⓘ

this entity surface form: Support-Vector Networks (1995)

Vladimir Vapnik → knownFor → Support Vector Machines ⓘ

this entity surface form: support vector machines

Vladimir Vapnik → coInvented → Support Vector Machines ⓘ