On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
The nature of statistical learning theory
The nature of statistical learning theory
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A robust minimax approach to classification
The Journal of Machine Learning Research
Some greedy learning algorithms for sparse regression and classification with mercer kernels
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Sparse probabilistic classifiers
Proceedings of the 24th international conference on Machine learning
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Coefficient Structure of Kernel Perceptrons and Support Vector Reduction
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Sparse Kernel Learning and the Relevance Units Machine
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using Kernel Basis with Relevance Vector Machine for Feature Selection
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
On-line independent support vector machines
Pattern Recognition
Greedy-based design of sparse two-stage SVMs for fast classification
Proceedings of the 29th DAGM conference on Pattern recognition
Sparse least squares support vector regressors trained in the reduced empirical feature space
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The minimum redundancy-maximum relevance approach to building sparse support vector machines
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Relevance units latent variable model and nonlinear dimensionality reduction
IEEE Transactions on Neural Networks
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Example-dependent basis vector selection for kernel-based classifiers
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Building sparse support vector machines for multi-instance classification
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Simplifying SVM with weighted LVQ algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Algorithms and Applications
InstanceRank based on borders for instance selection
Pattern Recognition
A sequential algorithm for sparse support vector classifiers
Pattern Recognition
Learning sparse kernel classifiers in the primal
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Multiple kernel local Fisher discriminant analysis for face recognition
Signal Processing
Regularized vector field learning with sparse approximation for mismatch removal
Pattern Recognition
Optimizing cepstral features for audio classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build sparse kernel learning algorithms by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting kernel machine is explicitly controlled while at the same time performance is kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the support vectom machine results in a concrete algorithm for building sparse large margin classifiers. These classifiers essentially find a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.