Sparseness of support vector machines
The Journal of Machine Learning Research
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
The Journal of Machine Learning Research
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
The Journal of Machine Learning Research
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
Learning SVM with Varied Example Cost: A kNN Evaluating Approach
Computational Intelligence and Security
Support Vector Machines, Data Reduction, and Approximate Kernel Matrices
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A Bahadur Representation of the Linear Support Vector Machine
The Journal of Machine Learning Research
Aggregation of SVM Classifiers Using Sobolev Spaces
The Journal of Machine Learning Research
Learning from dependent observations
Journal of Multivariate Analysis
Margin calibration in SVM class-imbalanced learning
Neurocomputing
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
Classification Using Geometric Level Sets
The Journal of Machine Learning Research
Multiclass support vector machines for adaptation in MIMO-OFDM wireless systems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Multi-weight vector projection support vector machines
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
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Function classes that approximate the bayes risk
COLT'06 Proceedings of the 19th annual conference on Learning Theory
On the consistency of multiclass classification methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Fast rates for support vector machines
COLT'05 Proceedings of the 18th annual conference on Learning Theory
A Distributional Interpretation of Robust Optimization
Mathematics of Operations Research
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Coherence functions with applications in large-margin classification methods
The Journal of Machine Learning Research
Conjugate relation between loss functions and uncertainty sets in classification problems
The Journal of Machine Learning Research
| Hi-index | 754.84 |
It is shown that various classifiers that are based on minimization of a regularized risk are universally consistent, i.e., they can asymptotically learn in every classification task. The role of the loss functions used in these algorithms is considered in detail. As an application of our general framework, several types of support vector machines (SVMs) as well as regularization networks are treated. Our methods combine techniques from stochastics, approximation theory, and functional analysis