A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
The Journal of Machine Learning Research
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
Neural Computation
Multi-kernel regularized classifiers
Journal of Complexity
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
The Journal of Machine Learning Research
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
The Journal of Machine Learning Research
A Sparse Sampling Method for Classification Based on Likelihood Factor
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Learning from dependent observations
Journal of Multivariate Analysis
Learning rates of gradient descent algorithm for classification
Journal of Computational and Applied Mathematics
Learning using hidden information (learning with teacher)
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Support vector machine for functional data classification
Neurocomputing
Online Learning with Samples Drawn from Non-identical Distributions
The Journal of Machine Learning Research
Consistency of functional learning methods based on derivatives
Pattern Recognition Letters
On qualitative robustness of support vector machines
Journal of Multivariate Analysis
Support Vector Machines with the Ramp Loss and the Hard Margin Loss
Operations Research
Improved working set selection for larank
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Support vector machines with beta-mixing input sequences
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Nonlinearity in Forecasting of High-Frequency Stock Returns
Computational Economics
Multitask multiclass support vector machines: Model and experiments
Pattern Recognition
| Hi-index | 0.00 |
We show that support vector machines of the 1-norm soft margin type are universally consistent provided that the regularization parameter is chosen in a distinct manner and the kernel belongs to a specific class-the so-called universal kernels-which has recently been considered by the author. In particular it is shown that the 1-norm soft margin classifier with Gaussian RBF kernel on a compact subset X of Rd and regularization parameter cn = nβ-1 is universally consistent, if n is the training set size and 0 β 1/d.