Comparing support vector machines with Gaussian kernels to radial basis function classifiers
Bernhard Schölkopf, Kah-Kay Sung low, Chris Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, Vladimir Vapnik
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Citation edge evidence
- A Tutorial on Support Vector Machines for Pattern Recognition→Comparing support vector machines with Gaussian kernels to radial basis function classifi…Legacy edge — source detail unavailable
- Least Squares Support Vector Machine Classifiers→Comparing support vector machines with Gaussian kernels to radial basis function classifi…Legacy edge — source detail unavailable
- On the algorithmic implementation of multiclass kernel-based vector machines→Comparing support vector machines with Gaussian kernels to radial basis function classifi…Legacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→Support-vector networksLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→Support-Vector NetworksLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→Least squares quantization in PCMLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
- Comparing support vector machines with Gaussian kernels to radial basis function classifi…→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy edge — source detail unavailable
- Least Squares Support Vector Machine Classifiers→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Support-Vector Networks→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Support-Vector Networks→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- A Tutorial on Support Vector Machines for Pattern Recognition→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- A Tutorial on Support Vector Machines for Pattern Recognition→Support-Vector NetworksLegacy edge — source detail unavailable
- A Tutorial on Support Vector Machines for Pattern Recognition→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy edge — source detail unavailable
- A Tutorial on Support Vector Machines for Pattern Recognition→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
- A Tutorial on Support Vector Machines for Pattern Recognition→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem→Support-Vector NetworksLegacy edge — source detail unavailable
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem→Support-vector networksLegacy edge — source detail unavailable
- On the algorithmic implementation of multiclass kernel-based vector machines→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- On the algorithmic implementation of multiclass kernel-based vector machines→Support-Vector NetworksLegacy edge — source detail unavailable
- On the algorithmic implementation of multiclass kernel-based vector machines→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
- Support-vector networks→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Support-vector networks→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Support-vector networks | 1995 | 41,028 |
| The Nature of Statistical Learning Theory | 1995 | 39,409 |
| Support-Vector Networks | 1995 | 33,914 |
| Learning representations by back-propagating errors | 1986 | 31,688 |
| Least squares quantization in PCM | 1982 | 15,893 |
| A training algorithm for optimal margin classifiers | 1992 | 11,664 |
| Nonlinear Component Analysis as a Kernel Eigenvalue Problem | 1998 | 8,138 |
| Support Vector Method for Function Approximation, Regression Estimation and Signal Proces… | 1996 | 2,704 |
What cites it, inside the corpus
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Topics
| Neural Networks and Applications | Computer Science |
| Face and Expression Recognition | Computer Science |
| Machine Learning and ELM | Computer Science |
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