Who Cited It

A training algorithm for optimal margin classifiers

1992 · 11,664 citations · 27 from inside this corpus

Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

A training algorithm for optimal margin classifiers (1992)A training algorithm for opti…Fast Learning in Networks of Locally-Tuned Processing Units (1989)Fast Learning in Networks of …Neural Information Processing Systems (2018)Neural Information Processing…Neural Networks and the Bias/Variance Dilemma (1992)Neural Networks and the Bias/…What Size Net Gives Valid Generalization? (1989)What Size Net Gives Valid Gen…Support-vector networks (1995)Support-vector networksSupport-Vector Networks (1995)Support-Vector NetworksA Tutorial on Support Vector Machines for Pattern Recognition (1998)A Tutorial on Support Vector …Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1998)Nonlinear Component Analysis …An overview of statistical learning theory (1999)An overview of statistical le…Estimating the Support of a High-Dimensional Distribution (2001)Estimating the Support of a H…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…A Practical Guide to Support Vector Classication (2008)A Practical Guide to Support …The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl… (2015)The Precision-Recall Plot Is …An introduction to kernel-based learning algorithms (2001)An introduction to kernel-bas…Prediction, Learning, and Games (2006)Prediction, Learning, and Gam…Understanding Machine Learning: From Theory To Algorithms (2015)Understanding Machine Learnin…A Short Introduction to Boosting (1999)A Short Introduction to Boost…Supervised learning with quantum-enhanced feature spaces (2019)Supervised learning with quan…Boosting the margin: a new explanation for the effectiveness of voting methods (1998)Boosting the margin: a new ex…Using AUC and accuracy in evaluating learning algorithms (2005)Using AUC and accuracy in eva…The Boosting Approach to Machine Learning: An Overview (2003)The Boosting Approach to Mach…Kernel principal component analysis (1997)Kernel principal component an…Word sense disambiguation (2009)Word sense disambiguationOn the algorithmic implementation of multiclass kernel-based vector machines (2002)On the algorithmic implementa…Boosting for transfer learning (2007)Boosting for transfer learningGeneralized Discriminant Analysis Using a Kernel Approach (2000)Generalized Discriminant Anal…Kernel methods in machine learning (2008)Kernel methods in machine lea…Machine learning predictive models for mineral prospectivity: An evaluation of neural net… (2015)Reconciling modern machine-learning practice and the classical bias–variance trade-off (2019)Reconciling modern machine-le…Inductive learning algorithms and representations for text categorization (1998)Inductive learning algorithms…Comparing support vector machines with Gaussian kernels to radial basis function classifi… (1997)Comparing support vector mach…
31 of 31 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Fast Learning in Networks of Locally-Tuned Processing Units19894,230
Neural Information Processing Systems20183,765
Neural Networks and the Bias/Variance Dilemma19923,612
What Size Net Gives Valid Generalization?19891,559

What cites it, inside the corpus

PaperYearCited
Support-vector networks199541,028
Support-Vector Networks199533,914
A Tutorial on Support Vector Machines for Pattern Recognition199816,488
Nonlinear Component Analysis as a Kernel Eigenvalue Problem19988,138
An overview of statistical learning theory19996,311
Estimating the Support of a High-Dimensional Distribution20016,097
Learning Deep Architectures for AI20095,077
A Practical Guide to Support Vector Classication20085,074
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…20155,051
An introduction to kernel-based learning algorithms20013,498
Prediction, Learning, and Games20063,279
Understanding Machine Learning: From Theory To Algorithms20153,081
A Short Introduction to Boosting19992,952
Supervised learning with quantum-enhanced feature spaces20192,548
Boosting the margin: a new explanation for the effectiveness of voting methods19982,344
Using AUC and accuracy in evaluating learning algorithms20052,163
The Boosting Approach to Machine Learning: An Overview20032,023
Kernel principal component analysis19971,950
Word sense disambiguation20091,832
On the algorithmic implementation of multiclass kernel-based vector machines20021,789
Boosting for transfer learning20071,748
Generalized Discriminant Analysis Using a Kernel Approach20001,683
Kernel methods in machine learning20081,618
Machine learning predictive models for mineral prospectivity: An evaluation of neural net…20151,570
Reconciling modern machine-learning practice and the classical bias–variance trade-off20191,561
Inductive learning algorithms and representations for text categorization19981,469
Comparing support vector machines with Gaussian kernels to radial basis function classifi…19971,414

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Topics

Neural Networks and ApplicationsComputer Science
Blind Source Separation TechniquesComputer Science
Optical Polarization and EllipsometryEngineering

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complete

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  • supports3 author record(s) attached.
  • supports31 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

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