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Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Sparse bayesian learning and the relevance vector machine
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Some greedy learning algorithms for sparse regression and classification with mercer kernels
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Sparseness of support vector machines
The Journal of Machine Learning Research
A study on reduced support vector machines
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A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Feature selection in a kernel space
Proceedings of the 24th international conference on Machine learning
A subspace kernel for nonlinear feature extraction
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Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Classifier complexity reduction by support vector pruning in kernel matrix learning
IWANN'07 Proceedings of the 9th international work 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
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ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Building a sparse kernel classifier on riemannian manifold
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ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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A sequential algorithm for sparse support vector classifiers
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Training sparse SVM on the core sets of fitting-planes
Neurocomputing
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This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The added constraint explicitly controls the sparseness of the classifier and an approach is provided to solve the formulated problem. When considering the dual of this problem. it can be seen that building an SLMC is equivalent to constructing an SVM with a modified kernel function. Further analysis of this kernel function indicates that the proposed approach essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace different classes of data are linearly well separated. Experimental results over several classification benchmarks show that in most cases the proposed approach outperforms the state-of-art sparse learning algorithms.