The nature of statistical learning theory
The nature of statistical learning theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
Convex Optimization
Combined SVM-Based Feature Selection and Classification
Machine Learning
Laplace maximum margin Markov networks
Proceedings of the 25th international conference on Machine learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Non-monotonic feature selection
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Optimal feature selection for support vector machines
Pattern Recognition
Maximum Entropy Discrimination Markov Networks
The Journal of Machine Learning Research
IEEE Transactions on Neural Networks
White box classification of dissimilarity data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Online feature selection for mining big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Fuzzy rough based regularization in Generalized Multiple Kernel Learning
Computers & Mathematics with Applications
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Although support vector machines (SVMs) for binary classification give rise to a decision rule that only relies on a subset of the training data points (support vectors), it will in general be based on all available features in the input space. We propose two direct, novel convex relaxations of a non-convex sparse SVM formulation that explicitly constrains the cardinality of the vector of feature weights. One relaxation results in a quadratically-constrained quadratic program (QCQP), while the second is based on a semidefinite programming (SDP) relaxation. The QCQP formulation can be interpreted as applying an adaptive soft-threshold on the SVM hyperplane, while the SDP formulation learns a weighted inner-product (i.e. a kernel) that results in a sparse hyperplane. Experimental results show an increase in sparsity while conserving the generalization performance compared to a standard as well as a linear programming SVM.