An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
Support vector machines are universally consistent
Journal of Complexity
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Neural Computation
Consistency of support vector machines and other regularized kernel classifiers
IEEE Transactions on Information Theory
On the optimal parameter choice for ν-support vector machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Some Properties of Regularized Kernel Methods
The Journal of Machine Learning Research
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
Stability of Unstable Learning Algorithms
Machine Learning
Training a Support Vector Machine in the Primal
Neural Computation
A Direct Method for Building Sparse Kernel Learning Algorithms
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
Sparse kernel SVMs via cutting-plane training
Machine Learning
Sparse Kernel SVMs via Cutting-Plane Training
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Robustness of reweighted Least Squares Kernel Based Regression
Journal of Multivariate Analysis
On-line independent support vector machines
Pattern Recognition
You live, you learn, you forget: continuous learning of visual places with a forgetting mechanism
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Prune support vector machines by an iterative process
International Journal of Computers and Applications
On the sparseness of 1-norm support vector machines
Neural Networks
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
The Journal of Machine Learning Research
Selecting a reduced set for building sparse support vector regression in the primal
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Resilient approximation of kernel classifiers
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Selection of basis functions guided by the L2 soft margin
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Efficient reduction of support vectors in kernel-based methods
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Condensed vector machines: learning fast machine for large data
IEEE Transactions on Neural Networks
The Journal of Machine Learning Research
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
On qualitative robustness of support vector machines
Journal of Multivariate Analysis
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A novel heuristic for building reduced-set SVMs using the self-organizing map
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Fast support vector machines for structural Kernels
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
The consistency analysis of coefficient regularized classification with convex loss
WSEAS Transactions on Mathematics
Fast opposite maps: an iterative SOM-Based method for building reduced-set SVMs
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Hybrid classifiers for object classification with a rich background
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Learning non-linear classifiers with a sparsity constraint using L1 regularization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Indexed block coordinate descent for large-scale linear classification with limited memory
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Coherence functions with applications in large-margin classification methods
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A fast algorithm for kernel 1-norm support vector machines
Knowledge-Based Systems
Asymmetric least squares support vector machine classifiers
Computational Statistics & Data Analysis
A bagging SVM to learn from positive and unlabeled examples
Pattern Recognition Letters
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Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our way we prove several results which are of great importance for the understanding of SVMs. In particular, we describe to which "limit" SVM decision functions tend, discuss the corresponding notion of convergence and provide some results on the stability of SVMs using subdifferential calculus in the associated reproducing kernel Hilbert space.