Support Vector Method for Function Approximation, Regression Estimation and Signal Processing
Vladimir Vapnik, Steven E. Golowich low, Alex Smola
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.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- 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
- Support Vector Machines for Classification and Regression→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy edge — source detail unavailable
- Generalized Discriminant Analysis Using a Kernel Approach→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy edge — source detail unavailable
- Kernel methods in machine learning→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy 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
- Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Support Vector Machines for Classification and Regression→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Generalized Discriminant Analysis Using a Kernel Approach→Support Vector Machines for Classification and RegressionLegacy edge — source detail unavailable
- Generalized Discriminant Analysis Using a Kernel Approach→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
- Generalized Discriminant Analysis Using a Kernel Approach→The Nature of Statistical Learning TheoryLegacy 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
- 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
- A Tutorial on Support Vector Machines for Pattern Recognition→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Kernel methods in machine learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| The Nature of Statistical Learning Theory | 1995 | 39,409 |
What cites it, inside the corpus
Links
Topics
| Neural Networks and Applications | Computer Science |
| Control Systems and Identification | Engineering |
| Image and Signal Denoising Methods | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports3 author record(s) attached.
- supports3 reference(s) recorded.
- neutralThe DOI carries no year to check against.
- supportsA title is present.
Provenance
sha256 46f669157f96ea35…